Global Estimation of Canopy Water Content

1
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
• ?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 (gt4)
• Agricultural Irrigation Scheduling
• Fuel Moisture Estimates for Wildfire Risk
  Prediction
• Soil Moisture (SMOS) Corrections for Vegetation

2
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,
ME Harvard Forest, MA WLEF-Tall Tower, WI Wind
River, WA Central California-Western Nevada
(mixed semiarid vegetation) Bondville, IL
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Effect of Leaf Biochemistry on Leaf Reflectance
Chlorophyll Structure Parameter Dry
Matter
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C.
Rueda, and S.L. Ustin
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Variation in Soil Reflectance
Soil background effect on canopy spectra
simulated by (a) PROSPECT-SAILH, (b)
PROSPECT-rowKUUSK, (c) PROSPECT-FLIM
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C.
Rueda, and S.L. Ustin
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Soil background reflectance on Simulated EWT and
Canopy Water Content (a) PROSPECT-SAILH (b)
PROSPECT-rowKUUSK (c) PROSPECT-FLIM
EWT
CwLAI (cm)
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C.
Rueda, and S.L. Ustin
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Comparison of Field Measured EWT and AVIRIS at
Walnut Gulch, AZ
Hunt et al.
Variation in EWT-AVIRIS By Vegetation Type
Yen-Ben Cheng, Susan L. Ustin, and David Riaño
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Cross Calibration between AVIRIS and MODIS
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Relationship between EWT-AVIRIS and MODIS
Indexes at 3 sites
AZCAL Properties, CA on 16 July 2002
Walnut Gulch, AZ on 25 August 2004
Howland forest, ME on 23 August 2002 
Yen-Ben Cheng, Susan L. Ustin, and David Riaño
(c)
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• EWT (AVIRIS)
• (b) NDWI (MODIS)
• (c) NDII (MODIS)

AZCAL Properties, CA
Walnut Gulch, AZ
Howland Forest, ME
Y-B Cheng, S.L. Ustin, and D. Riaño
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MODIS-NDWI Time Series Variation with Land
Cover Classes
MODIS NDWI Index
Palacios-Orueta et al.
Time, 2000-2005
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Neural Net Prediction (ANN) of EWT
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 r and 210 t

Riaño et al. (r20.95)
r, t
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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
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 EWTLAI
• ANN run on MODIS product MOD09A1
• AVIRIS EWT Used for Validation

Walnut Gulch in AZ
AVIRIS EWT
R2 0.82
AVIRIS MODIS NDWI
MODIS EWT
NDVI, NDWI, NDW6
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C.
Rueda, L. Usero, S.L. Ustin
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Predicting Fuel Moisture Content for Wildfire
Risk Assessment
Estimated by PROSPECT from LOPEX Fresh Leaf Data
P-valuelt0.0001
Measured Dry Matter (g/cm2)
Measured EWT (g/cm2)
Equivalent Water Thickness (g/cm2)
Dry matter (g/cm2)
Generalized additive algorithm-partial least
square regression, GA-PLS
Lin Li, Susan Ustin, and David Riaño