NASA Review (71007)

1

High Resolution Soil Moisture Estimation via Data Assimilation Using

NASA Land Information System

Valentine Anantharaj Georgy Mostovoy Anish Turlapaty and Jim Aanstoos

Mississippi State University - GeoResources Institute

NASA Review (71007)

2

LIS Evaluation Team amp Collaborators

bull RPC Teamndash Valentine Anantharaj Georgy Mostovoy Nicholas

Younan Jim Aanstoos and Anish Turlapaty (MSU)ndash Christa Peters-Lidard (NASA GSFC HSB)ndash Paul Houser (GMU CREW)ndash Bailing Li and Sujay Kumar (GSFC)

bull Collaborators and Consultantsndash USDA NRCSndash MSU DREC and USDA (Stoneville MS)

NASA Review (71007)

3

Identified Needs of USDA NRCS

bull Routine analysis soil moisture over the continental needs

watersoilssunweatherclimatevegetationterrain

watersoilssunweatherclimatevegetationterrain

observe model assimilateobserve model assimilate

NASA Review (71007)

4

Soil Moisture Data Sources in this RPC Experiment

bull In-situ observationsndash USDA Soil Climate Analysis Network (SCAN)

bull Remotely sensed and estimatedndash NASA and JAXA Aqua Advanced Scanning

Microwave Radiometer ndash EOS (AMSR-E)

bull Numerical Modelsndash The Noah model in the NASA Land Information

System

NASA Review (71007)

5

USDA NRCS SCAN

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

2

LIS Evaluation Team amp Collaborators

bull RPC Teamndash Valentine Anantharaj Georgy Mostovoy Nicholas

Younan Jim Aanstoos and Anish Turlapaty (MSU)ndash Christa Peters-Lidard (NASA GSFC HSB)ndash Paul Houser (GMU CREW)ndash Bailing Li and Sujay Kumar (GSFC)

bull Collaborators and Consultantsndash USDA NRCSndash MSU DREC and USDA (Stoneville MS)

NASA Review (71007)

3

Identified Needs of USDA NRCS

bull Routine analysis soil moisture over the continental needs

watersoilssunweatherclimatevegetationterrain

watersoilssunweatherclimatevegetationterrain

observe model assimilateobserve model assimilate

NASA Review (71007)

4

Soil Moisture Data Sources in this RPC Experiment

bull In-situ observationsndash USDA Soil Climate Analysis Network (SCAN)

bull Remotely sensed and estimatedndash NASA and JAXA Aqua Advanced Scanning

Microwave Radiometer ndash EOS (AMSR-E)

bull Numerical Modelsndash The Noah model in the NASA Land Information

System

NASA Review (71007)

5

USDA NRCS SCAN

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

3

Identified Needs of USDA NRCS

bull Routine analysis soil moisture over the continental needs

watersoilssunweatherclimatevegetationterrain

watersoilssunweatherclimatevegetationterrain

observe model assimilateobserve model assimilate

NASA Review (71007)

4

Soil Moisture Data Sources in this RPC Experiment

bull In-situ observationsndash USDA Soil Climate Analysis Network (SCAN)

bull Remotely sensed and estimatedndash NASA and JAXA Aqua Advanced Scanning

Microwave Radiometer ndash EOS (AMSR-E)

bull Numerical Modelsndash The Noah model in the NASA Land Information

System

NASA Review (71007)

5

USDA NRCS SCAN

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

4

Soil Moisture Data Sources in this RPC Experiment

bull In-situ observationsndash USDA Soil Climate Analysis Network (SCAN)

bull Remotely sensed and estimatedndash NASA and JAXA Aqua Advanced Scanning

Microwave Radiometer ndash EOS (AMSR-E)

bull Numerical Modelsndash The Noah model in the NASA Land Information

System

NASA Review (71007)

5

USDA NRCS SCAN

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

5

USDA NRCS SCAN

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

6

Anticipated Societal Benefits

1 provides critical information to support drought monitoring and mitigation

2 provides essential information for predicting droughts based on weather and climate predictions

3 supports irrigation water management4 supports fire risk assessment5 supports water supply forecasting and NWS flood forecasting6 supplies a critical missing component to assist with snow climate

and associated hydrometeorological data analysis7 supports climate change assessment8 enables water quality monitoring9 supports a wide variety of natural resource management amp research

activities such as NASA remote sensing activities of soil moisture and ARS watershed studies

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

7

An Integrated Framework forLand Data Assimilation System

ApplicationsInputs OutputsPhysics

TopographySoils

WaterSupply ampDemand

AgricultureHydro-ElectricPower

EcologicalForecasting

Water Quality

ImprovedShort Term

ampLong TermPredictions

Land Cover and Vegetation (MODIS AMSRTRMM SRTM)

Meteorology Modeled amp

Observed (TRMM GOES Station)

Observed Land States(Snow ET Soil Moisture Water

Carbon etc)

Land Surface Models (LSM)Physical Process Models

Noah CLM VIC SiB2 Mosaic Catchment etc

Data Assimilation Modules(EnKF EKF)Rule-based

Water Fluxes Runoff

Surface States

Moisture Carbon Ts

Energy FluxesLe amp H

Biogeo-chemistry

Carbon Nitrogen etc

(Peters-Lidard Houser Kumar Tian Geiger)

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

8

LIS Evaluations Purpose and Activities

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

9

Purpose of RPC Evaluations hellip

bull Primaryndash Evaluate LIS capabilities and NASA data to enhance

and extend USDA-NRCS SCANbull Approach

ndash Evaluate LIS performancendash Assimilate SCAN and AMSR-E observations and

evaluate LIS capabilities to enhance SCAN by means of Observation Sensitivity Experiments (OSE)

ndash Derive physically consistent soil moisture maps at a range of spatial resolutions from 25x25 km2 to 1x1 km2

ndash Quantify uncertainties at all scales

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

10

Team Activity

bull MsState Project Management RPC Integration Control Run MODIS-VF [SSURGO]

bull NASA GSFC LIS Support AMSR-E data assimilation science expertise

bull GMU CREW SCAN data assimilation science expertise

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

11

Data Assimilation and Observation Sensitivity Experiments

bull Evaluation of data assimilation techniquesndash EKF EnKF

bull Data assimilation (land state)ndash Soil moisture

bull Soil moisture stationsbull AMSR-E

ndash Temperaturebull MODIS LST []

bull Sensitivity studiesbull Expected Outcomes high resolution soil moisture

analysis product uncertainty characterization

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

12

Status of Current Activities

bull Preliminary evaluation of simulated soil moisture data ndash Georgy Mostovoy

bull Quality Assessment of soil moisture measurements AMSR-E and SCAN - Anish Turlapaty

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

13

Future Directions

bull Assimilate AMSR-E soil moisture datandash Evaluate AMSR-E impacts

bull Incorporate MODIS Vegetation Fraction (VF) and compare with control runndash Evaluate MODIS VF impacts

bull Assimilate SCAN soil moisture datandash Evaluate SCAN impacts

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

14

ASMR-E Soil Moisture Data Assimilation and Evaluation

Noah Land Surface Model of NASA Land Information System

Soil Moisture Data

Soil Climate Analysis Network

AMSR-Eon NASA

AQUA Satellite

Evaluation Study

Soil Moisture Data

Soil Moisture Data

Soil Moisture Data

No D

A

EnKF DA

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

15

Future plansAssimilation of AMSR-E soil moisture data

12 hour time step 3 hourly output and 5 ensemble members

00Z 03Z 06Z 09Z 12Z 15Z 18Z 21Z 00Z

12 hr forecast+obs 12 hr forecast+obs

Data assimilation frequency will be twice daily at 06Z and 18Z DADA will will not be ldquoturned onrdquo until observation is available not be ldquoturned onrdquo until observation is available We plan to take the ensemble mean as first guess for next time step initial conditions

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

16

Noah LSM RUN AMSR-E SM EnKF Assimilation(TEST2)

Scaled AMSR-E SM

Expected Result [Example Only]EnKF Assimilation of AMSR-E SM Retrievals

Noah LSM RUN

EnKF Assimilation of Scaled AMSR-E SM RetrievalsEnKF Assimilation (TEST2)

Example

Only

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

17

Preliminary Evaluation of Soil Moisture Simulated by the Noah

Land Surface Model

Georgy Mostovoy

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Geographical distribution of SCAN sites

OBJECTIVE Validation of the Noah Land Surface Model (LSM) baseline runsversus SCAN soil moisture observations

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

P ndash dominant (across var seasons) positive bias (high temporal variability of SM drying is rather rapid)N ndash dominant negative bias (SM exhibits low variability sluggish behavior in comparison with other sites)0 ndash zero bias

P

P

P

P

P

P

N

N

N

N

0

0

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Silver City MS Marianna AR

a flat terrain prevails

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

DPEt

w

E - evaporation rate (calculated based on air 2-m T q P and wind speed from NLDAS)P ndash precipitation rate (prescribed from the NLDAS data)D ndash exchange rate with adjacent soil layers (calculated based on soil type and w)

Total water content (w) within a soil layer of an arbitrary depth (10 cm 1 m or 2 m for example)

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

DtwDtEww ttt )1(11

1 Analogy with AR(1) process or the Markov chain

Considering a drying stage (P = 0)

where 1 twE

and α is evaporation efficiency

)1()( ttR is the autocorrelation functionvalue for the time lag Δt

For an arbitrary time t (t = n Δt n = 01 2 hellip) the autocorrelation function is defined as follows

)()(SMT

tEXPtR

))1(1(ln t

tTSM

is the integral correlation scale which defines the soil moisture ldquomemoryrdquo

Relationship between the correlation scale and evaporation efficiency (Δt = 1 day was used)

Given this simple model the evaporation term controls the soil moisture memory

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

DPEt

w

)(

2 An equation for the soil moisture error δw

An accumulated soil moisture error for the time period T can be written as follows

TTT

T DPEw000

)(

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Persistency (ldquomemoryrdquo) of soil moisture initial anomalies during Fall 2005

1 Weak memory case an initial positive anomaly between two SCAN sites disappears after about 40 days

2 Strong memory case an initial positive anomaly persists and amplifies during 40-days

bull Both cases suggest that local factors such as soil physical properties water table etc control dynamics of soil moisture anomalies (deviations from a regionally-mean)

bull This also implies a little control of precipitation on the initial soil moisture state and its dynamics over the Mississippi Delta Region(Maximum soil moisture values are bounded by the field capacity = the soil porosity)

bull Sub-monthly time scales are considered (2-3 weeks periods)

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Observed soil moisture (averaged over 12 SCAN sites) evolution at different depths during Fall 2005

error bars stand for standard deviation (SD)

Low SD

HighSD

Wet state -gt low SD

Dry state -gt high SD

Example of soil moisture comparison (averaged over 12 SCAN sites) between SCAN and simulated

by Noah

SM underestimation

O v e r e s t i m a t i o n(evaporation deficiency of the Noah model)

Precipitation event

Drying out

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Outline for baseline soil moisture simulations over the MS Delta region (I)

Noah Land Surface Model (v 271) available from LIS (v 431) was used for retrospective runs (years 2005 and 2006)

bull 1-km domain size 256x256 points (255x255 latitude-longitude)

North American Land Data Assimilation System (NLDAS Cosgrove et al 2003)

atmospheric forcing was used (specified at approx 15-km grid)

1-km 5-km and 15-km horizontal grid for the Noah model runs

(no substantial difference in 5-cm soil moisture content between 1-km 5-km and 15-km runs was

observed)

Statsgo Soil Data

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Outline for baseline soil moisture simulations over the MS Delta region (II)

One year (2004) spin-up period was used for the Noah model

bull Soil moisture observations available from 12 SCAN sites (subjective QC visual inspection of

plots) were used for validation of the baseline simulations (daily-

mean values of SM were compared)

bull Frequency distributions of soil moisture and precipitation

errors were plotted for two-month periods (March-April May-June July-August Sept-Oct)

spanning years 2005 and 2006

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Gap and scale change in the data

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

May-June 2005

P

P

PP

PP

0

P ndash dominant (across var seasons) positive biasN ndash dominant negative bias0 ndash zero bias

N

N N

0

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

May-June 2006

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Sept-Oct 2005

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Sept-Oct 2006

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

March-April 2005

Frequency distribution of soil moisture content error (5-cm daily-mean value simulated by Noah model at 1-km grid minus observed at SCAN site) and difference between NLDAS precipitation forcing

and observed local values at SCAN sites

Numbers in upper right corners stand for total number of precipitation events registered either by observations or by NLDAS data (upper row) within two-month period mean difference or bias and standard deviation between simulated and observed soil moisture (middle and lower rows respectively)

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

No apparent relationship was found between precipitation and 5-cm SM biases (measured as simulated minus observed value) from examination of error distribution histograms

Left frame shows a scatterplot between simulated minus observed maximum SM within each two-month period and a corresponding precipitation error bias The scatterplot suggests no significant linear relationship (a correlation coefficient R = -012 is not significant) between the precipitation and SM errors for maximum values of SM

Error sensitivity of simulated maximum 5-cm SM content to error of the NLDAS

precipitation forcing

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

No apparent association between soil texture (and its variability with depth presented in the left lower frame) and a sign of the SM bias was observed Conversely horizontal distribution patterns of simulated SM is controlled by corresponding patterns of the soil texture map (figures not presented)

Because of relatively high persistence (across different years and months) of a bias sign at a particular scan site it is possible to stratify all scan sites into three category (with significant positive negative and smallzero bias) according to this sign Specifically six sites (N Issaquena Perthshire Farm Tunica Earle Campus and Lonoke Farm) demonstrated rather persistent positive SM bias At four sites such as Beasley Lake Vance Marianna and DeWitt) a negative SM bias was dominant Two sites (Silver City and Good Timber Creek) showed a relatively small SM bias

Both positive and negative significant SM biases occurred mostly during drying stages of soilsrsquo matter This fact suggests that an accurate descriptionspecification of other factors (such as upperlower boundary conditions for the SM atmospheric evaporation etc) in addition to precipitation forcing is critical for reduction of the SM error

Soil texture

Soil texture vertical heterogeneity

(numbers indicate scan sites)

Dominant positive SM bias ndash dotted lines

Dominant negative or ldquozerordquo ndash solid lines

4 ndash Silt Loam 8 ndash Silty Clay Loam 9 ndash Clay Loam11 ndash Silty Clay

Local samples versus Statsgo data

Impact on 5-cm SM bias

Increase of clay content

Decr

ease

of

sand

con

ten

t w

ith d

ep

th

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Soil texture variations with depth Seasonality of soil moisture biases at 5-cm depth

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Summary

o The Noah model performance was evaluated against soil moisture observations at 12 sites during the years 2005 and 2006

o The Noah model demonstrates a reasonable skill over the Mississippi Delta Region with a typical bias of 5 The Noah model produces an overestimation during drying out periods This deficiency might be fixed by adding the assimilation capability of soil moisture observations (SCAN and AMSR-E) into the Noah model

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

38

Quality Assessment of AMSR-E Soil Moisture Data

Anish Turlapaty

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

39

PROBLEM DESCRIPTION

AMSR-E

Noah Land Surface Model of

NASA Land Information

System

Soil Moisture Data

Assimilation

Data Validation Before data assimilation AMSR-E soil moisture data product has to be validated

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

40

GENERAL APPROACH

bull Test Data Soil moisture data is collected from AMSR-E for the years 2005 2006 for Mississippi and Arkansas

bull Training Data For validation purposes soil moisture data from 20 locations of SCAN network in Mississippi and Arkansas is used

bull Quality control tool One class support vector machines which provide a quality value for each time series

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

RESULTS Quality Map

SVM method

Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

Quality Maps Contd

QC values are assigned at each pixel (28x23)Invalid data

1

Poor data2

Marginal quality

3

Marginal quality

4

Good quality data

5

Remarks on Quality

Quality Level

Mahalanobis Method

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

43

SUMMARY

Quality maps are developed for Mississippi and Arkansas which show the quality of time series at each pixel on scale of five to one

These results are compared with quality map from Mahalanobis method

Currently we are looking for a conventional quality control tool with which these results can be verified

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

44

Questions

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135

NASA Review (71007)

45

Contact Information

Valentine Anantharajltvalgrimsstateedugt

Tel (662)325-5135