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Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain

Rapid Prototyping Capability for Earth-Sun Systems Sciences. Evaluating Next Generation NASA Earth Science Observations for Image Fusion to Enable Mapping Variation in Soil Moisture at High Resolution. Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain

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Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain

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  1. Rapid Prototyping Capability for Earth-Sun Systems Sciences Evaluating Next Generation NASA Earth Science Observations for Image Fusion to Enable Mapping Variation in Soil Moisture at High Resolution Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain University of Mississippi Geoinformatics Center The University of Mississippi

  2. PROJECT TEAM • The University of Mississippi • Greg Easson, PhD • Robert Holt, PhD • A. K. M. Azad Hossain • Stennis Team • Robert Ryan, Ph.D. • Alaska Satellite Facility • Don Atwood, Ph.D. • Sandia National Laboratories • Mr. Michael B. Hillesheim • Consulting Geologist • Dennis Powers, Ph.D. 2 of 25

  3. OUTLINE • Purpose and Scope • Study Site • Potential Decision Support Tools • Data Used • RPC Experiments • Preliminary Results • Project Status 3 of 25

  4. PURPOSE AND SCOPE Mapping soil moisture at high resolution? • Mapping soil moisture at both high spatial and temporal resolution not possible due to lack of sensors with these combined capabilities Virtual Soil Moisture Sensor (VSMS)! • We hypothesize that MODIS can be transformed to virtual soil moisture sensors (VSMS) for mapping soil moisture at high spatial and temporal resolution by: • Fusion with SAR data (VSMS1) • Disaggregation model (VSMS2) Rapid Prototyping Capability (RPC) Project • We designed a RPC project to evaluate potential of Visible Infrared Imager Radiometer Suite (VIIRS) to replace MODIS to improve monitoring soil moisture by generating VSMS 4 of 25

  5. MODIS VS. VIIRS 5 of 25

  6. STUDY SITE • Part of Nash Draw in southeastern New Mexico. • Project site is a part of Chihuahuan Desert. Site extent: approximately 400 sq. km. • Semi-arid area • Karst topography Location of Nash Draw (Holt et al., 2005) 6 of 25

  7. POTENTIAL DECISION SUPPORT TOOLS • Universal Triangle Model (VI-LST Triangle Model) • for soil moisture estimation • Regression and Artificial Neural Network (ANN) based models • for soil moisture prediction at high resolution (VSMS generation) • Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) • for soil moisture estimation • Radiative Transfer Model (RTM) and DisaggNet • for disaggregation of coarse resolution soil moisture imagery (VSMS generation) 7 of 25

  8. DATA USED • MODIS • 13 scenes, daily reflectance (MOD09GQK) at 250 m and daily land surface temperature product (MOD11) at 1 km resolution • VIIRS • Simulated bands I1 and I2 at 400 m resolution for MOD09 and bands M15 and M16 at 800 m resolution for MOD11 • Radarsat 1 SAR • 4 Fine Beam imagery at 8 m resolution and 37o incidence angle • AMSR-E • Level 3 soil moisture product (AE_Land3) at 25 km resolution for corresponding MODIS/VIIRS data • Field Data • 2 sets of 80 soil samples collected within a site covering 225 sq. km in Nash Draw to measure volumetric soil moisture • DEM • Digital elevation model (DEM) obtained at 30 m resolution 8 of 25

  9. DATA USED Image Acquisition Dates 9 of 25

  10. FORMULATION CHART System Model: VI-LST Triangle Model, Regression, ANN, SHEELS, RTM and DisaggNet • Benefits: • Mapping recharge zones at karst topography, which is critical for the hydrologic models of the area • Soil moisture input for other decision support systems (SWAT/AWARD/ • PECAD) Prediction and Measurements: Soil moisture at high resolution (10 m/daily) Decision Support: AWARD SWAT PECAD • Earth Observations: • MODIS Reflectance • MODIS Thermal • VIIRS Reflectance • VIIRS Thermal • AMSR-E Soil Moisture • RADARSAT 1 SAR Fine • Field Data 10 of 25

  11. RPC EXPERIMENTS Three RPC experiments in the project • Experiment 1: Soil Moisture Estimation • Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model • Experiment 2: Generation of VSMS1 • Evaluate VIIRS to replace MODIS in virtual soil moisture generation using Multiple Regression and ANN with SAR • Experiment 3: Generation of VSMS2 • Evaluate VIIRS to replace MODIS in virtual soil moisture generation using SHEELS, RTM and DisaggNet 11 of 25

  12. RPC EXPERIMENT # 1 NDVI MODIS SM(1km) LST R LST: Land Surface Temperature AMSR-E SM R: Regression • Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model MODIS

  13. RPC EXPERIMENT # 1 • VI-LST Triangle model by Carlson et al. (1994) • Relationship between soil moisture M, VI (NDVI), and LST (T) can be expressed through a regression formula 13 of 25

  14. RPC EXPERIMENT # 1 NDVI MODIS SM(1km) MODIS LST R AMSR-E SM • Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model R: Regression LST: Land Surface Temperature 14 of 25

  15. RPC EXPERIMENT # 1 NDVI VIIRS SM(1km) VIIRS LST R AMSR-E SM • Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model R: Regression LST: Land Surface Temperature 15 of 25

  16. RPC EXPERIMENT # 2 Field Data R SAR SM (10 m) R ANN VSMS1M SM (10 m) R: Regression SM: Soil Moisture ANN: Artificial Neural Network VSMS: Virtual Soil Moisture Sensor • Goal: Evaluate VIIRS to replace MODIS in virtual soil moisture sensor (VSMS1) generation using Multiple Regression and ANN with SAR MODIS SM (1 km) SAR Imagery 16 of 25

  17. RPC EXPERIMENT # 2 Field Data R SAR SM (10 m) R ANN VSMS1V SM (10 m) R: Regression SM: Soil Moisture ANN: Artificial Neural Network VSMS: Virtual Soil Moisture Sensor • Goal: Evaluate VIIRS to replace MODIS in virtual soil moisture sensor (VSMS1) generation using Multiple Regression and ANN with SAR VIIRS SM (1 km) SAR Imagery 17 of 25

  18. EVALUATION OF VIIRS TO MODIS RPC Experiment # 1 (Soil Moisture Estimation) • Correlation co-efficient (R) between field observed soil moisture and MODIS/ VIIRS derived soil moisture • Uncertainty analysis using field observed soil moisture and MODIS/VIIRS derived soil moisture Where, U = uncertainty, A= measurement accuracy, and P = precision µ= the average of all the measured values Xi corresponds to a true value T 18 of 25

  19. EVALUATION OF VIIRS TO MODIS RPC Experiment # 2 (VSMS Generation) • Mean Absolute Percent Error (MAPE) between MODIS derived VSMS and VIIRS derived VSMS. • MAPE is a pixel by pixel error evaluation technique between predicted and observed values. • We will consider MODIS as the observed value and VIIRS as the predicted value. Where, VSMSV and NSMM refer to soil moisture derived from virtual soil moisture sensor for VIIRS and MODIS respectively; n is the total number of pixels in a polygon 19 of 25

  20. PRELIMINARY RESULTS • Soil Sample Locations in the Study Site • Samples analyzed for volumetric soil moisture measurements 20 of 25

  21. PRELIMINARY RESULTS 21 of 25

  22. PROJECT STATUS • Subcontracts • ASF, Stennis Team, SNL and Dr. Powers • Paper works completed • Data Collection • MODIS data • Reflectance – Acquired • Thermal- Acquired • AMSR-E data • Level 3 soil moisture product-Acquired • VIIRS Data • Simulation pending 22 of 25

  23. PROJECT STATUS • Data Collection • SAR data • Fine Beam data- Acquired • Field Data • Soil samples acquired twice • Data Analysis • Sample analysis for soil moisture measurement • Completed • SAR data preprocessing • On going • Soil moisture estimation • On going 23 of 25

  24. PROJECT SCHEDULE

  25. Thank You! 25 of 25

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