2011 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Aihua Li liaihua33413@sina.comYanchen Bo boyc@bnu.edu.cnLing Chen chenling8247@126.com

Bayesian Maximum Entropy Data Fusion of Field Observed LAI and Landsat ETM+ Derived LAI

State Key Laboratory of Remote Sensing Science, Beijing, ChinaBeijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing Normal University, Beijing, ChinaSchool of Geography, Beijing Normal University,Beijing, China

Outline

1. Introduction

2. Methodology

3. Application(Data)

4. Results

5. Discussion and Conclusions

1. IntroductionThe leaf area index (LAI) characterizes the condition of vegetation growth and is a key input parameter of land-surface-dynamic-process models.

Several LAI products are accessible from different thermal sensors

MODIS

Sensor Spatial resolution

Time resolution

Time coverage Ref.

MODIS 1KM 8 Days 2000-now Myneni et al. 2002

MISR 1KM 8 Days 2000-now Knyazikhin et al. 1998, Hu et al. 2003

VEGETATION 1KM 10 Days 1998-now Baret et al. 2007, Weiss et al. 2007, Deng et al. 2006

AVHRR 0.25° 30 Days 1981-2001 Chen et al. 2002

POLDER 6KM 10 Days 11/1996-06/199704/2003-10/2003

Roujean and Lacaze 2002, Lacaze 2005

These moderate resolution LAI products should be validated before application (Justice and Townshend 1994, Cihlar et al. 1997, Liang 2004)

1. Introduction

In-situ measurements • Heterogeneity makes pixel scale

validation not simply equivalent to field measurements average(Liang et al. 2002)

• The accuracy of geostatistics methods to obtain LAI surface maps is limited to the number and the spatial distribution of measurement points.

High resolution LAI surface• Extensive cover regions• Lower accuracy

Current Situation

MODIS

Landsat

LAI2000

combine

Problems are solved by combining these two types of data

Field LAI measurements and high resolution LAI surface maps are two kinds of so-called “true” data

1. Introduction

Accurate high resolution LAI reference maps are needed for the validation of coarser resolution satellite derived LAI

Regression analysis and Geostatistical methods: do not take account of the uncertainties of measurements and models

The uncertainties of obtained data and information are taken into account in the fusion, the result will be more objective

Need

MODIS

Landsat

LAI2000

combine

Problem

Our work:Integrating the ETM+ derived LAI and field measurements LAI based on BME

2. Methodology

• Soft data: non-accurate ； Hard data: accurate

• Soft data can be expressed in terms of interval values and probability statements in mathematical computation (Christakos 2000)

Soft data and hard data

BME : Probabilistic method

• It can take account of the uncertainties associated with measurements and models.

• In BME, the uncertainty is considered when the input data are not accurate.

Study Sites

Harvard Forest (HARV) LTERMixed hardwoods, Eastern hemlock,Red pine, Old-field meadow

Bondville Agricultural Farmland (AGRO)Corn, Soybeans, Fallow

Konza Prairie Biological Station (KONZ) LTERTallgrass, Shortgrass, Shrub, Gallery forest; grazing and burning regimes

3. Application

Data

Sites Datasets used Data obtained time

HARVField measurements 2000-06-18,08-04ETM+ LAI 2000-08-04ETM+ Land cover 2000

AGROField measurements 2000-05,07,08ETM+ LAI 2000-07-14,08-11ETM+ Land cover 2000

KONZ

Field measurements2000-06-07 to 06-082000-08-25 to 08-272000-10-12 to 10-13

ETM+ LAI 2000-06-06 to 082000-08-25 to 27

ETM+ Land cover 2000

Specifications of HARV site, AGRO site and KONZ site

3. Application

Creating soft data

• Field measurements based on ETM+ derived LAI• Variance of residuals • Interval soft data(Upper boundary and lower boundary)

• Multiple field measurements can be processed as Gaussian probability soft data

1. Multiple measurements

2. Linear regression model

The regression model (trend line in red color) for Field LAI and corresponding ETM+ LAI: HARV

(left), AGRO (middle), KONZ (right)

a estimationLAI LAI

estimationbLAI LAI

Site Slope Intercept R2

HARV 0.61 1.92 0.41AGRO 0.98 0.0484 0.86KONZ 0.59 0.835 0.29

Interval soft data

2

Selected Soft data

The interval ETM+ LAI data (red and green, solid line is about the mean values) and the Gaussian probability field measurements data (blue): HARV (left), AGRO (middle), KONZ (right)

3. Application

The nested covariance models of different vegetation types

Biome Nested Covariance Models Parameters

evergreen neddleleaf forestdeciduous broadleaf forestmixed forestgrasslandopen shrublandcornsoybean

11

3expnuggets

sC s c ca

22

3exps

sca

11

3expNuggets

sC s c ca

3

2 32 2

3sph 12 2s s

s sca a

1 1 2 20.1; 0.7; 750; 0.8; 250;Nugget s sc c a c a

1 1 2 20.1; 0.4; 1100; 0.6; 250;Nugget s sc c a c a

1 1 2 20.03; 0.4; 1100; 0.27; 250;Nugget s sc c a c a

1 1 2 20.1; 0.5; 1500; 0.45; 400;Nugget s sc c a c a

1 1 2 20.01; 0.7; 1800; 0.2; 500;Nugget s sc c a c a

1 1 2 20.1; 0.3; 750; 1.2; 900;Nugget s sc c a c a

1 1 2 20.09; 0.25; 950; 1.0; 750;Nugget s sc c a c a

Parameters of covariance models

covariance models

Three cases based on BMEmethods Input data

BMEintervalModeETM+ LAI: Interval soft dataIn-situ LAI : hard data

the difference between maximum and mean estimation

BMEprobMoments1ETM+ LAI: Interval soft dataIn-situ LAI : hard data

the difference between hard field measurements and soft field measurementsBMEprobMoments1

ETM+ LAI: probability soft dataIn-situ LAI : probability data

3. Application

4. Results

Prediction maps and original ETM+ LAI maps have very similar spatial pattern and distribution trend

ETM+ LAI surface, BMEintervalMode, BMEprobMoments1 and BMEprobMoments2 prediction surfaces are shown from left to right respectively: HARV (up), AGRO (middle), KONZ (bottom)

Predicted LAI1, Predicted LAI2 and Predicted LAI3 are the results of BMEintervalMode, BMEprobMoments1 and BMEprobMoments2 respectively.

4. Results

Summary statistics of LAI predictions compared to field measurements

Sites Num. of plots Methods R2 RMSE Bias CR VO

HARV 48

ETM+ LAI 0.57 0.688 -0.054 0.754 1.290BMEintervalMode 0.59 0.518 -0.030 0.770 0.996BMEprobMoments1 0.59 0.344 0.014 0.766 0.656BMEprobMoments2 0.57 0.351 -0.311 0.754 0.657

AGRO 19

ETM+ LAI 0.82 0.631 0.049 0.905 1.070BMEintervalMode 0.89 0.46 0.099 0.942 0.988BMEprobMoments1 0.84 0.582 0.062 0.917 1.050BMEprobMoments2 0.82 0.623 -0.215 0.906 1.060

KONZ 19

ETM+ LAI 0.45 0.436 -0.275 0.669 1.100BMEintervalMode 0.89 0.114 -0.043 0.945 0.657BMEprobMoments1 0.78 0.157 -0.062 0.883 0.628BMEprobMoments2 0.45 0.216 -0.376 0.671 0.548

R2 and CR of BME methods are higher than those of ETM+ derived LAI and RMSE of BME is lower than those of ETM+ derived LAI. Bias is reduced by BMEintervalMode

and BMEprobMoments1.VO of BME method is less than that of ETM+ derived LAI.

5. Discussion and ConclusionsBME can:• Get rid of some extreme data and lower the RMSE and result in

small variance. • Take account of the uncertainties associated with measurements

and models• Combine data at different scale

• However, field measurements for validation should not be used in inversion, but in this work, some field measurements may be applied in both validation and inversion.

• Further study can be done in LAI inversion by linking high resolution remotely sensed imagery with field measurements to explore the potential of BME.

July 27, 2011

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