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 (>4)
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

3. Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin
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

4. 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

5. Soil background reflectance on Simulated EWT and Canopy Water Content
(a) PROSPECT-SAILH (b) PROSPECT-rowKUUSK (c) PROSPECT-FLIM
EWT
Cw*LAI (cm)
Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin

6. 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

7. Cross Calibration between AVIRIS and MODIS

8. 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)

9. 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

10. MODIS-NDWI Time Series
Variation with Land Cover
Classes
MODIS NDWI Index
Palacios-Orueta et al.
Time,

11. Neural Net Prediction (ANN) of EWT
Training
Dataset
Validation
Real Data
MODIS
Leaf Training Leaf Validation Application
LOPEX data
PROSPECT
Both
PROSPECT- SAILH
AVIRIS
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda, L. Usero, S.L. Ustin

12. ANN trained with Real Data at Leaf Level for EWT
Trained with all LOPEX samples
Leave one out cross-validation
420 input layers: 210 r and 210 t
420 Input Layers
Hidden Layer with varying numbers of neurons
Output Layer
EWT
Riaño et al. (r2=0.95)
r, t
The number of neurons is selected interactively until an error accuracy is met. We used a method of controlled trial and error to optimize the network structure. ~100 neurons used. Different numbers of neurons in the hidden layer probed with different initial weights.

13. Analysis at canopy level
Trained with PROSPECT-SAILH: 600 random samples
Validation with PROSPECT-SAILH: 7400 samples independent of training
PROSPECT-SAIH
EWT, LAI, DM
N, Cab, LIDF, Soil
canopy r
1. Radiative Transfer model
EWT*LAI
2. Training ANN
canopy r
3. Validation
canopy ρ
EWT*LAI
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

14. 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
Walnut Gulch in AZ
AVIRIS EWT
R2 = 0.82
AVIRIS MODIS NDWI
MODIS EWT
D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda, L. Usero, S.L. Ustin
NDVI, NDWI, NDW6

15. Predicting Fuel Moisture Content for Wildfire Risk Assessment
Estimated by PROSPECT from LOPEX Fresh Leaf Data
P-value<0.0001
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
PROSPECT estimating EWT and dry weight from FRESH leaves. It is not possible to get DM accurately form PROSPECT in fresh leaves (although it is accurately predicted in dry leaves).
New methods seem promising: This one is a generic algorithm-partiial least squares regression produced by my co-author Lin Li.
In this example, the Mean reflectance at each band is subtracted, then divided by SD before running model.
Later in this meeting I will present a neural net analysis from another co-author, David Riano, who finds similar results for EWT (I am not presenting his DM results) using this technique. Both are currently working on applying these methods to images as a way to more consistently extract biophysical information from complex images with changing properties in space/time.
Lin Li, Susan Ustin, and David Riaño