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Global Estimation of Canopy Water Content Susan Ustin (PI), UC Davis E. Raymond Hunt (Co-PI) USDA Water Lab Vern Vanderbilt (Co-PI) NASA Ames Research Center. Goals: (1) Test and Validate Retrieval of Water Content (2) Evaluate Ecological Value of Water Content Index - PowerPoint PPT Presentation
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Global Estimation of Canopy Water ContentSusan Ustin (PI), UC Davis
E. Raymond Hunt (Co-PI) USDA Water Lab
Vern Vanderbilt (Co-PI) NASA Ames Research Center
Goals: (1) Test and Validate Retrieval of Water Content (2) Evaluate Ecological Value of Water Content Index
►Theoretical Evaluations at Leaf and Canopy Scales • Evaluate effect of cover, vegetation type, and soil background
►Empirical Evaluations• Compare to Field Data• Compare to AVIRIS EWT• Compare to VIs under Different Land Cover Conditions
►Testing Ecological Information• Plant Water Stress/Drought Indicator• Estimate LAI at High LAI sites (>4)• Agricultural Irrigation Scheduling• Fuel Moisture Estimates for Wildfire Risk Prediction• Soil Moisture (SMOS) Corrections for Vegetation
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Field Research Sites:
Wind River Ameriflux Site (mature conifer)SMEX 04 southern Arizona and Northern Mexico (semiarid)SMEX 05 agriculture, Ames, Iowa (corn, soybean)Agriculture, San Joaquin Valley, CA (cotton)
Analysis of MODIS Time Series Data at Ameriflux Sites:
Howland, MEHarvard Forest, MAWLEF-Tall Tower, WIWind River, WACentral California-Western Nevada (mixed semiarid vegetation)Bondville, IL
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0.00
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400 900 1400 1900 2400Wavelength (nm)
Ref
lect
ance
Cm=0.001g cm-2
Cm=0.015g cm-2
Cm=0.030g cm-2
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0.25
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400 900 1400 1900 2400Wavelength (nm)
Refle
ctanc
e
N=1.5
N=1.0
N=0.5
0.00
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400 900 1400 1900 2400Wavelength (nm)
Ref
lect
ance
Cab=20
Cab=40
Cab=80
Chlorophyll Structure Parameter Dry Matter
Effect of Leaf Biochemistry on Leaf Reflectance
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin
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400 900 1400 1900 2400Wavelength (nm)
Ref
lect
ance
Dark Soil Medium Soil Bright Soil
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400 900 1400 1900 2400Wavelength (nm)
Ref
lect
ance
Dark soil
Medium soil
Bright soil
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400 900 1400 1900 2400
Wavelength (nm)
Ref
lect
ance
Medium soil
Dark soil
Bright soil
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0.3
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400 900 1400 1900 2400
Wavelength (nm)R
efle
ctan
ce
Dark soil
Bright soil
Medium soil
Soil background effect on canopy spectra simulated by (a) PROSPECT-SAILH, (b) PROSPECT-rowKUUSK, (c) PROSPECT-FLIM
Variation in Soil Reflectance
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin
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0
0.05
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0.25
0.3
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16
medium
dark
bright
0
0.02
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0.1
0.12
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0 0.05 0.1 0.15 0.2
medium
dark
bright
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0 0.05 0.1 0.15 0.2
medium
dark
bright
Soil background reflectance on Simulated EWT and Canopy Water Content
(a) PROSPECT-SAILH (b) PROSPECT-rowKUUSK (c) PROSPECT-FLIM
Cw*LAI (cm)
EW
T
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin
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y = 7.9019x + 88.181
r2 = 0.90
0
1000
2000
3000
4000
0 100 200 300 400 500Canopy EWT (um)
AV
IRIS
EW
T (
um)
<20%
20-40%
>40%
n=19 n=15 n=10
n=20n=12
n=12
n=22
0
500
1000
1500
2000
2500
3000
3500
4000
EW
T (
um)
Comparison of Field Measured EWT and AVIRIS at Walnut Gulch, AZ
Variation in EWT-AVIRIS By Vegetation Type
y = 878.87x + 428.47
r2 = 0.82
0
1000
2000
3000
4000
0 1 2 3 4 5LAI
AV
IRIS
EW
T (
um)
Agriculture
Native species
(b)
Yen-Ben Cheng, Susan L. Ustin, and David Riaño
Hunt et al.
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NDVIy = 1.0652x - 0.081
r2 = 0.6081
EVIy = 0.9483x + 0.0137
r2 = 0.7081
SIWSIy = 0.9222x + 0.0337
r2 = 0.7137
NDWIy = 0.9494x + 0.004
r2 = 0.433
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1Cross-calibrated AVIRIS indexes
MO
DIS
inde
xes
NDWI
SIWSI
NDVI
EVI
Cross Calibration between AVIRIS and MODIS
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y = 0.0002x + 0.3348
R2 = 0.5044
y = 0.0002x + 0.0624
R2 = 0.6358
y = 0.0001x + 0.0173
R2 = 0.5732
y = 7E-05x - 0.0701
R2 = 0.5365
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500
NDWI
SIWSI
NDVI
EVI
Linear (NDVI)
Linear (EVI)
Linear (SIWSI)
Linear (NDWI)
y = 0.0002x + 0.1528
R2 = 0.8865
y = 8E-05x + 0.1146
R2 = 0.823
y = 0.0001x - 0.1807
R2 = 0.812
y = 4E-05x - 0.129
R2 = 0.6186
-0.3
-0.2
-0.1
0
0.1
0.2
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0.5
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0 500 1000 1500 2000
NDWI
SIWSI
NDVI
EVI
y = 5E-05x + 0.7182
R2 = 0.4852
y = 5E-05x + 0.405
R2 = 0.5505
y = 8E-05x + 0.11
R2 = 0.6612
y = 4E-05x - 0.0821
R2 = 0.5853
0
0.1
0.2
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0.5
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0.8
0.9
1
0 500 1000 1500 2000 2500 3000 3500
NDWI
SIWSI
NDVI
EVI
Linear (NDVI)
Linear (EVI)
Linear (SIWSI)
Linear (NDWI)
Walnut Gulch, AZ on 25 August 2004
Relationship between EWT-AVIRIS and MODIS Indexes at 3 sites
AZCAL Properties, CA on 16 July 2002
Howland forest, ME on 23 August 2002
Yen-Ben Cheng, Susan L. Ustin, and David Riaño
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Walnut Gulch, AZ
(a) EWT (AVIRIS) (b) NDWI (MODIS) (c) NDII (MODIS)
Howland Forest, ME
AZCAL Properties, CA
Y-B Cheng, S.L. Ustin, and D. Riaño
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MODIS-NDWI Time Series
Variation with Land CoverClasses
Time, 2000-2005
MO
DIS
ND
WI
Inde
x
Palacios-Orueta et al.
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Neural Net Prediction (ANN) of EWT
Training Dataset
Validation Dataset
Real DataMODIS
Leaf Training Leaf Validation Application
Input
Input
Input
Output ANN
Input
Input
Input
Output ANN
Input
Input
Input
Output ANN
LOPEX dataPROSPECT
Both
LOPEX dataPROSPECT
PROSPECT-SAILHAVIRIS
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda, L. Usero, S.L. Ustin
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ANN trained with Real Data at Leaf Levelfor EWT
• Trained with all LOPEX samples • Leave one out cross-validation• 420 input layers: 210 and 210
420 Input Layers
Hidden Layer with varying numbers of neurons
Output Layer
EWT
Riaño et al. (r2=0.95)
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Analysis at canopy level• Trained with PROSPECT-SAILH: 600 random
samples • Validation with PROSPECT-SAILH: 7400
samples independent of training
210 Input Layers
Hidden Layer with variant number of neurons
Output Layer
PROSPECT-SAIH
EWT, LAI, DMN, Cab, LIDF, Soil
canopy
1. Radiative Transfer model
EWT*LAI
2. Training ANNcanopy
3. Validationcanopy ρ
EWT*LAI
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda L. Usero, S.L. Ustin
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Analysis at Canopy Level with MODIS
• ANN trained with PROSPECT-SAILH
to generate EWT*LAI• ANN run on MODIS product
MOD09A1 • AVIRIS EWT Used for Validation
AVIRIS MODIS NDWI
Walnut Gulch in AZ
NDVI, NDWI, NDW6
MODIS EWT
AV
IRIS
EW
T
R2 = 0.82
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda, L. Usero, S.L. Ustin
15-1.0
1.0
3.0
5.0
7.0
9.0
11.0
-1.0 1.0 3.0 5.0 7.0 9.0 11.0
Measured %FMC
1:1 line
Training
Prediction
-0.01
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0.01
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0.04
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0.06
-0.01 0.00 0.01 0.02 0.03 0.04 0.05 0.06
Measured EWT
Est
imat
ed E
WT
1:1 line
Training
Prediction
-0.001
0.004
0.009
0.014
0.019
-0.001 0.004 0.009 0.014 0.019
Measured DM
Es
timat
ed
DM
1:1 line
Training
Prediction
y = 0.8243x + 0.0006
R2 = 0.9478
0.00
0.01
0.01
0.02
0.02
0.03
0.03
0.04
0.04
0.05
0.05
0 0.01 0.02 0.03 0.04 0.05 0.06
Equivalent Water Thickness (g/cm2)
Measu
red
EW
T (
g/c
m2)
Mea
sure
d D
ry M
atte
r (g
/cm
2)
Dry matter (g/cm2)
y = 0.8308x + 0.0009
R2 = 0.342
0.00
0.01
0.01
0.02
0.02
0.03
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016
Predicting Fuel Moisture Content for Wildfire Risk Assessment
Estimated by PROSPECT from LOPEX Fresh Leaf Data
Generalized additive algorithm-partial least square regression, GA-PLS
Lin Li, Susan Ustin, and David Riaño
P-value<0.0001