1st year meeting --- Progress Report

wp-2: Satellite observations of vegetation cover, of surface albedo and temperature over the Plateau

Guangjian Yan

State Key Laboratory of Remote Sensing Science,School of Geography, Beijing Normal University

2009-6-29

Objectives

Develop algorithms to retrieve vegetation cover, LAI, surface albedo, emissivity and land surface temperatures from measurements by polar and/or geostationary satellites;

Produce a consistent data set for the land surface variables over the region of Tibetan Plateau.

Partners LSIIT - Université Louis Pasteur (ULP) The International Institute for Geo-information Scien

ce and Earth Observation (ITC) Alterra Green World Research - Wageningen Univer

sity and Research Centre (Alterra) University of Valencia(UVEG) Beijing Normal University(BNU) Institute of Geographic Sciences and Natural Resou

rces Research (IGSNRR) Institute of Remote Sensing Applications(IRSA)

Tasks Task 2.1. A generic algorithm for the retrieval of important surface pr

operties from a multitude satellite sensors (ULP, ITC, IRSA)

Task 2.2 Developing an algorithm for simultaneous retrievals of atmospheric variables, land surface variables using AATSR bi-angular/multi-spectral radiometric observations (Alterra, IGSNRR).

Task 2.3. Improving MODIS LAI and albedo products by combining remote sensing model and dynamic process model (BNU, IRSA)

Task 2.4. Developing an algorithm to produce a consistent LST from polar satellites (LSIIT, UVEG, BNU, IGSNRR, IRSA).

Task 2.5. Estimation of LSE, LST and albedo from Geostationary Satellite (GS) data (LSIIT, Alterra, IGSNRR)

Task 2.1. A generic algorithm for the retrieval of important surface properties from a multitude satellite sensors

Model Algorithm data parameter

Optical/Laser

Albedo

Baysian inversion

Elevation

Synergy of active and passive optical data

Optical

Coupled RT modeling

LAI

Albedo modling for terrain

Albedo retrieval algorithm

★ ★★ Partly finished

★

Task 2.2. Developing an algorithm for simultaneous retrievals of atmospheric variables, land surface variables using AATSR data

Model Algorithm data parameter

LSCT FVC

Algorithm developing for AATSR

Albedo

AATSR

LAI Water vapour AOD

Task 2.3. Improving MODIS LAI and albedo products by combining remote sensing model and dynamic process model

Model Algorithm data parameter

MISR TM

Albedo fCover

Angular effect correction

Assimilation method

LUT based RT model inversion

HJ-1

LAI trend

LAI

MODIS

ASTER

A priori knowledge

based inversion

★ ★ ★★ Partly finished

★ ★

Task 2.4. Developing an algorithm to produce a consistent LST from polar satellites

Model Algorithm data parameter

TDC modeling

Temporal normalization

of LST

AMSR-EHJ-1

LST

Algorithm developing

for HJ-1

RT modeling for canopy and terrain

MODIS

LST retrieval algorithms for all

weather conditions

★ ★★ Partly finished

★

Task 2.5. Estimation of LSE, LST and albedo from Geostationary Satellite (GS) data

LSTAlbedo

Split-window algorithm adaption

Atmospheric correction scheme using TDC model and ECMWF data

ECMWF standard output

Geostationary satellite

Albedo retrieval algorithm

Model Algorithm data parameter

Main goals: measuring ice sheet mass b

alance, cloud and aerosol heights, land topography and vegetat

ion characteristics.

Organization: NASAAltitude: 600kmLaunch: 2003Life time: 5 yearsPayload: GLAS instrument

Elevation retrieval - ICESat Facts

Greenland 5 km DEM

Processing steps

Gaussian FittingPre-processingWaveform Dataset

Normalization

Conversion• Bin-ASCII• Counts-Voltage

Smoothing

GLA01

GLA14

Extraction of Elevation

Points

Initial estimation of

Gaussians

Gaussian Fitting

Parameters

Applications

Parameters

Results of Gaussian decomposition

Red: raw waveformGreen: Gaussian components

First mode: left most Gaussian component Last mode: right most

0 200 400 600-0.01

0

0.01

0.02

0.03

0.04Myfit-date:27-02-2003-49338080:19

Vo

lts

(V)

Relative Time (ns)0 200 400 600

-0.01

0

0.01

0.02

0.03

0.04Myfit-date:30-09-2003-235714742:1

Vo

lts

(V)

Relative Time (ns)

Angular&spectral kernel model to describe land surface BRDF

Albedo retrieval using HJ-1 data

•Here,C0CgCv,are kernel coefficients independent from wavelength and stand for the weights of different scattering parts. •They are all related to the structure of canopy or mixed pixel. •Although all the kernel coefficients have clear physical meaning, they can also be treated as empirical parameters.

Compute the pixel-average slope and aspect angle for each 500m grid

Compute the subpixel correction factor T for each 5km grid

Compute the pixel-average slope and aspect angle for each 5km grid

90m resolution D

EM

over the Plateau

Store the results in database

Flow chart for setting up a database for topography effect correction.

500m/5km resolution direction reflectance from MODIS or HJ-1A/1B

Correct the pixel level topography effect,using slope and aspect angle.

Inversion of ASK BRDF model

Derive spectral albedo and broadband albedo

500m/5km resolution topography corrected albedo

Database of topographic param

eters

Flow chart for topography effect correction for 500m albedo products.

LAI retrieval -- a priori knowledge based inversion

0

1

2

3

4

5

6

0. 2 0. 4 0. 6 0. 8

NDVI

LAI

LAI = a*e0. 046x

R2 = 0. 9751

0

1

2

3

4

5

6

0 20 40 60

Ral ati ve growth date

LAI

parameter spaceparameter space time seriestime series

0

0. 5

1

1. 5

2

2. 5

11 13 15 17 19Date (Apri l )

ME o

f in

vert

ed L

AINo a pri oriknowl edge

A pri oriknowl edgei ncl ude growthrel ati onshi pA pri oriknowl edgei ncl ude VIrel ati onshi pAl l of the apri oriknowl edge

NW5

NW4

NW3

NW2NW1

Shunyi county, Beijing, 2001.

NW5

NW4

NW3

NW2NW1

(using only red band ） (using only NIR band)

LAI maps without VI-based a priori knowledge

NW5

NW4

NW3

NW2NW1

(using 2 bands with VI-based a priori knowledge)

(using both red and NIR but without VI-based a priori knowledge)

Time series LAI retrieval by coupling crop growth model

Relative leaf area index (RLAI: LAI/LAImax) from 865 ground measure

ments in Shunyi and Changping of Beijing were fitted with relative a

ccumulation temperature (DVS).

0. 0

0. 2

0. 4

0. 6

0. 8

1. 0

1. 2

0 0. 5 1 1. 5 2

DVS

RLAI

2max

2.5

I 1 (7.76 12.75* 5.63* )i

i i

LAI

LA EXP DVS DVS

Empirical LOGISTIC model for crop growth

Time series LAI retrieval -- flowchart

LAImax,A0(A2)

Weather dataC,A0(A2),A1

LOGISTIC

LAImax

A0(A2)

The prior knowledge of LAImax and A0(A2)

Sun Zenith Angle,Sensor Zenith Angle,Sun Azimuth Angle,Sensor

Azimuth Angle,LAD,hotspot factor,the ratio of skylight

SAILH

Leaf ReflectanceLeaf TransmitanceSoil Reflectance

The prior knowledge of Leaf ReflectanceLeaf Transmitance

and Soil Reflectance

Cost Function

Optimized by SCE-UA

Optimal parameter sets:

Leaf ReflectanceLeaf TransmitanceSoil Reflectance

Time Series Reflectance

Measured Times Series Reflectance

Time Series LAI

The initial value and the bound value of the

retrieval parameters

RLAI between 0 and 1

LOGISTIC

Time Series LAI(the first periods)

Obtain the Time Series LAI of the

second ,the third and fourth periods using

the same method

Obtainthe

averagevalue of thefour

periods

Result

Validation using ground measured LAI values in Shunyi, Beijing, 2001

LAI retrieval -- Data assimilation Developing a priori LAI trend from several years’

MODIS LAI product the adaptive Savitzky-Golay filtering to eliminate the contaminat

ed pixels.

the SARIMA method is used to construct the dynamic model.

The Ensemble Kalman Filter technique is discussed to est

imate real-time LAI from time series MODIS reflectance

data.

Adaptive Savitzky-Golay (SG)

0 23 46 69 92 115 138

T ime

0.0

1.0

2.0

3.0

4.0

5.0

6.0

LA

I (m

2 /m

2 )

M O D IS LA I

S G filte ring LA I

0.0

0.2

0.4

0.6

0.8

1.0N

DV

IN D VI

N D VI Enve lope

C ontam inated obs

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361

D A Y/2001

NDVI temporal profiles with circles to mark the contaminated data ( ) _ ( ) _ ( )NDVI i NDVI Env i NDVI Env i

Dynamic model the following dynamic model is constructed based on the climatology

to evolve LAI in time and used to provide the short-range forecast of LAI

1LAI LAIt t tF clim

clim

LAI11

LAIt

t

t

dF

dt

Bondville

0.0

1.0

2.0

3.0

4.0

5.0

LA

I (m

2 /m

2 )

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361

D A Y/2008

R etrieved LA I

M O D IS LA I

F ie ld LA I

0.0

1.0

2.0

3.0

4.0

5.0L

AI(

m2 /

m2 )

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361

D A Y/2001

R etrieved LA I

M O D IS LA I

F ie ld LA ITonzi Ranch

Linze Yingke

(MODIS+MISR)

0.0

1.0

2.0

3.0

4.0

5.0

LA

I(m

2 /m

2 )

1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361

D A Y/2001

R etrieved LA I-M O DIS+M ISR

R etrieved LA I-M O DIS

M O D IS LA I

F ie ld LA I

M O D IS observations

M ISR observations

Fractional vegetation cover retrieval – angular correction A general formula to calculate fCover

A simple model for mixing pixles:

min

max min

( ) ( )( )

( ) ( )

NDVI NDVIf

NDVI NDVI

( ) ( )f a NDVI b

-LAI G( ) /cos( ) (1 )f S e

eLAI =0.5 LAI 1-S S

θ

Model inversion using multitude remote sensing data with two spatial resolutions MODIS observations are used for their angular

information

Higher resolution images (HJ-1, TM) are used to get S

solve equations for a, b, LAIe, then calculate fcover

e 1-LAI /cos( )1( ) (1 )a NDVI b S e

e 2-LAI /cos( )2( ) (1 )a NDVI b S e

e-LAI /cos( )( ) (1 )m

ma NDVI b S e

Vegetation detection

HJ reflectancesred&NIR

MODIS reflectances red&NIR

fcover retrieval

LAIe

estimate S factor

Geolocation match

fcover

MODIS geometry

Cloud detection

Study area (Heihe basin)

Gebi desert

Zhangye City

Angular effects corrected fCover (Mar. 07 2009)

LAI and fCover generation using high resolution images

To evaluate the uncertainty in the prediction of the biophysical variables (LAI and fCover) due to the coarse spatial resolution of the MODIS sensor by using high resolution optical data

Particular relevance will be given to the temporal profile of LAI and fCover along the different seasons and according to the main and back-up MODIS algorithms.

LAI and fCover estimation procedure

High resolution LAI and fCover ma

psLUT based RTM inversion

(PROSPECT+SAILH)

Sensor Date ASTER 24 July 2001 29 November 2001 12 March 2002 Landsat TM 9 June 2002 28 August 2002 2 December 2002 24 March 2003

High resolution TOC reflectance

Validation procedure

Field measurements

?

High resolution LAI and fCover

maps

Aggregated LAI and fCover maps

Uncertaintiesassessment

Comparison with MODIS LAI and fCover prod

ucts

Sensor Date ASTER 24 July 2001 29 November 2001 12 March 2002 Landsat TM 9 June 2002 28 August 2002 2 December 2002 24 March 2003

More data available ?

Comparison:MODIS – high resolution data

Probability density functions (pdf) of LAI and fCover estimated from high resolution data are determined for a sufficient number of MODIS pixels of different land use.

Based on these analyses, uncertainty assessment of parameter retrievals is carried out separately for the four major native pasturelands, identified from the southeast to northwest: bush-meadow, alpine meadow, alpine grassland and desert grassland.

Physics-based Radiation Transfer Canopy Model for All Growth Stages

LST retrieval -- Modeling

R o w dire c t io n

Inte ge ral r ange

r 0

R o w c e ntral l ine

P

d p

Top view of the wheat canopy

a periodic function to discuss the mutual overlaps between the neighboring rows.

simplify the row planted wheat canopy as foliage gather whose density is unchanged along row direction and in the vertical

direction, and is changed gradually in the crossing-row direction.

Brightness temperature directional distribution simulated by the new model.

Directional thermal radiation from rugged terrain

affect spatial distribution of vegetation

obstruct sky radiance

geometric effect on surface radiation

shadowing effect

increase environmental radiation

temperature difference on shadowed and sunlit surfaces

A parameterization scheme for coarse resolution pixels

1 ） emitted radiation of surface

2 ） environmental and sky radiation

i iR

i

L AL

A

( )

( )

i i i sky i cH

i

t sky c

C L Vd L AL

A

C L VdL

calculate the statistics in a coarse pixel

( )i RL L

introduce an effective view angle

average ratio that obstruction surroundings occupied in view hemisphere of surface

averaged sky factor

account for emitted directional radiation of a coarse pixel

( ) ( )

cos cos( ) (1 )LAI G LAI G

v sL L e L e

2 / 2

0 0

2 / 2

0 0

1/ 22

arccos cos ( )

arccos cos cos sin sin cos( ) ( )

arccos cos 1 tan

a S dSd

S S A a S dSd

S

1. θ

1. S

a simple model for angular anisotropy effect

supposed L(θ) has a linear relationship with cos , with Hapke formula (1993)

parameterization for sky factor vd and terrain configuration factor Ct

slope S, azimuth A is the horizon angle for direction （ Dozier and Frew, 1990 ）。 integrating over the coarse pixel

2 / 2 01

2 0 0 / 2( ) ( ) ( )vd vd k H dH P d a S dSdA

H

pdf of elevation

/ 2

0

1/ 22

1 cos( )

2

1 11 tan

2 2

t

SC a S dS vd

S vd

pdf of horizon angle Hφ pdf of slope angle

preliminary validation

error caused by simplized form of surface emitted radiance (K)

error of radiance measured at surface (K)

1 、 Followed by a statistical parameterization scheme, a model was developed for remote sensing retrieval. 2 、 All the simplifications calculate statistics of the topographic effects exerted on radiative transfer.3 、 The parameterized model cost much less time with an acceptable accuracy lost.

HJ-1 150m LST products was proposed by the IRSA/CAS as a daytime land product over the Tibet Plateau.

A view angle dependent single channel LST algorithm has been developed for correcting atmospheric and emissivity effects for all land cover types.

HJ satellite constellation

LST retrieval using HJ-1 data

CCDresolution ： 30mbands ： 0.43-0.52μm ， 0.52-0.60μm ， 0.63-0.69μm ， 0.76-0.90μmswath ： ≥ 700kmIRSresolution ： 150m （ NIR MIR ） /300m （ TIR ）bands ： 0.75-1.10μm ， 1.55-1.75μm ， 3.50-3.90μm ， 10.5-12.5μmswath ： 720km

Supposing that the atmosphere is uniform on spherical surface and layered vertically, the atmospheric transmittance can be expressed as (Fan et al, 2007):

where η is the path factor, R is the earth radius, s0 is the atmosphere layer thickness, θ is the view zenith angle.

(1) Obtaining the atmospheric parameters

•simulation results indicate that there are strong linear relationships between different L↑(θ) and L↑(0) (nadir view)

HJ-1 LST

Plots of Atmospheric parameters against the water vapor content

(a) atmospheric transmittance (b) atmospheric upwelling radiance(c) atmospheric downwelling radiance

(2) Obtaining the land surface emissivity For HJ-1 IRS data we will use the NDVI method to estimate land

surface emissivity. (3) Retrieving the land surface temperature

HJ-1 LST

Validation related works

Ground-based LAI measurement methods comparison

LAI-2000

Direct LAI measurement

Indirect LAI measurement

Digital Hemisphreical Photography (DHP)

The comparision of LAI2000 data and FishEye data with moving average fitting. Obviously ， the variance of LAI2000 data is littler than that of Hemispherical photography data.

:is the woody-to-total area ratio, it was used to correct the woody components effects in LAI measurements. :the effective LAI :the needle-to-shoot area ratio :is the elements clumping index

(1 ) /e e eL L

eL

e

e

Indirect estimation of forest canopy LAI correct forest LAI by Multispectral Canopy

Imager (MCI)

1st year meeting --- Progress Report

VIS NIR

Classified image

Sky fractionWoody components fractionLeafy fraction

statistical methods for:

:the effective plant area index at zenith angle :the effective woody area index at zenith angle:the proportion of elements within the image:the proportion of woody components within the image

Sky Fraction, Woody components Fraction, Leafy Fraction (VZN 0~90°interval 10°) ：

/2

0

( ) 2 ln[ ( )] sin( )e tPAI p d

The effective PAI:

/2

0

( ) 2 ln[ ( )] sin( )e wWAI p d

The effective WAI:

10

1

2e i iP PW

( )ePAI

( )eWAI

( )tp ( )wp

:the effective PAI at zenith angle iP :the weight at zenith angleiW

/WAI PAI

Stand 1: 0.1909Stand 10: 0.1798α ： 0.1816

:the woody area index:the plant area index

WAI

PAI

1st year meeting --- Progress Report

Thank you!