1. 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. 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. 3 Chlorophyll Structure ParameterDry Matter Effect of Leaf Biochemistry on Leaf Reflectance Y-B. Cheng, P.J. Zarco-Tejada, D. Riaño, C. Rueda, and S.L. Ustin

4. 4 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

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

6. 6 Comparison of Field Measured EWT and AVIRIS at Walnut Gulch, AZ Variation in EWT-AVIRIS By Vegetation Type Yen-Ben Cheng, Susan L. Ustin, and David Riaño Hunt et al.

7. 7 Cross Calibration between AVIRIS and MODIS

8. 8 (c) 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

9. 9 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

10. 10 MODIS-NDWI Time Series Variation with Land Cover Classes Time, 2000-2005 MODIS NDWI Index Palacios-Orueta et al.

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

12. 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  and 210  420 Input Layers Hidden Layer with varying numbers of neurons Output Layer EWT  Riaño et al. (r 2 =0.95)

13. 13 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, DM N, Cab, LIDF, Soil canopy  1. Radiative Transfer model EWT*LAI 2. Training ANN canopy  3. Validation canopy ρ EWT*LAI D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda L. Usero, S.L. Ustin

14. 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 AVIRIS MODIS NDWI Walnut Gulch in AZ NDVI, NDWI, NDW6 MODIS EWT AVIRIS EWT R 2 = 0.82 D. Riaño, M.A. Patricio, P. Zarco-Tejada, C. Rueda, L. Usero, S.L. Ustin

15. 15 Equivalent Water Thickness (g/cm 2 ) Measured EWT (g/cm 2 ) Measured Dry Matter (g/cm 2 ) Dry matter (g/cm 2 ) 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