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Global Estimation of Canopy Water Content Susan Ustin (PI), UC Davis

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

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Global Estimation of Canopy Water Content Susan Ustin (PI), UC Davis

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  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. 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, 2000-2005

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

  12. 420 Input Layers Hidden Layer with varying numbers of neurons Output Layer EWT 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. (r2=0.95) r, t

  13. 1. Radiative Transfer model 2. Training ANN 3. Validation canopy r canopy ρ EWT, LAI,DM N, Cab, LIDF, Soil PROSPECT-SAIH EWT*LAI EWT*LAI canopy r 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

  14. Analysis at Canopy Level with MODIS Walnut Gulch in AZ • ANN trained with PROSPECT-SAILH to generate EWT*LAI • ANN run on MODIS product MOD09A1 • AVIRIS EWT Used for Validation 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

  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 Lin Li, Susan Ustin, and David Riaño

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