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Greg Easson, Ph.D.- ( PI) Robert Holt, Ph.D.- ( Co-PI) A. K. M. Azad Hossain Patrick Yamnik

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. Project Team. Greg Easson, Ph.D.- ( PI) Robert Holt, Ph.D.- ( Co-PI) A. K. M. Azad Hossain

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Greg Easson, Ph.D.- ( PI) Robert Holt, Ph.D.- ( Co-PI) A. K. M. Azad Hossain Patrick Yamnik

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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 Project Team Greg Easson, Ph.D.- (PI) Robert Holt, Ph.D.- (Co-PI) A. K. M. Azad Hossain Patrick Yamnik University of Mississippi Geoinformatics Center The University of Mississippi Collaborators

  2. Outline • Project Overview • Objectives • Study Site • RPC Experiments • Project Progress • Data Collection/Processing • Data Analysis and Results • Upcoming Major Tasks • Schedule/Current Status • Future Prospects 2 of 21

  3. Problem Statement Mapping Soil Moisture • Not possible at both high spatial and temporal resolution due to lack of sensors with these combined capabilities 3 of 21

  4. Project Overview - Objectives 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 VIIRS to replace MODIS to improve monitoring soil moisture by generating VSMS 4 of 21

  5. Project Overview – Study Site • Part of Nash Draw in southeastern New Mexico • Project site is a part of Chihuahuan Desert. Site extent: approximately 225 sq. km • Semi-arid area • Karst topography 5 of 21

  6. Project Overview – 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 6 of 21

  7. Project Overview – Experiment # 1 MODIS Reflectance Data (MOD09) Thermal Data (MOD11) LST NDVI AMSR-E VI- LST Triangle Model SM R SM-Soil Moisture R- Regression VI-Vegetation Index MODIS SM(1 km) Estimation of MODIS/VIIRS Based Soil Moisture Simulated Reflectance Simulated Thermal Data VIIRS SM(800 m) NDVI-Normalized Difference Vegetation Index LST-Land Surface Temperature 7 of 21

  8. Project Overview – Experiment # 2 SAR-Synthetic Aperture Radar SM-Soil Moisture ANN-Artificial Neural Network R-Regression SAR Fine Imagery (10 m) SAR SM(10 m) R VIIRS SM (800 m) ANN RADARSAT 1 R VSMS1-Virtual Soil Moisture Sensor M-MODIS V-VIIRS VSMS1M SM(10 m) VSMS1V SM(10 m) Generation of Virtual Soil Moisture Sensor 1 (VSMS1) MODIS SM(1 km) Field Data 8 of 21

  9. Project Overview – Experiment # 3 SHEELS RTM SM VIIRS SM (800 m) MODIS SM(1 km) DisaggNet Emissivity VSMS2 (M) SM(10 m) VSMS2 (V) SM(10 m) Generation of Virtual Soil Moisture Sensor 2 (VSMS2) SHEELS - Simulator for Hydrology and Energy Exchange at the Land Surface RTM - Radiative Transfer Model DisaggNet - Neural network based Disaggregation model SM - Soil Moisture VSMS2 - Virtual Soil Moisture Sensor 2 M - MODIS V - VIIRS 9 of 21

  10. Project Progress - Data Data Collection and Processing 10 of 21

  11. Project Progress – Analysis/Results MODIS/VIIRS Based Soil Moisture Estimation (Exp. # 1) MOD11 1km FLST MOD09 250 m FNDVI AMSR-E L3 25 km SM R 1 km SM VI-LST Triangle Model 11 of 21

  12. Project Progress – Analysis/Results AMSR-E Soil Moisture (25 km resolution) 7.6 7.7 6.6 6.9 MODIS/VIIRS Based Soil Moisture Estimation (Exp.#1) 12 of 21

  13. Project Progress – Analysis/Results R2 = 0.45 R2 = 0.71 R2 = 0.47 R2 = 0.17 R2 = 0.71 R2 = 0.71 R2 = 0.60 R2 = 0.66 R2 = 0.66 R2 = 0.68 R2 = 0.53 R2 = 0.66 Comparison between VIIRS and MODIS NDVI (EXP. # 1) 13 of 21

  14. Project Progress – Analysis/Results Estimation of SAR Based Soil Moisture (Exp. # 2) • Empirical model for mapping soil moisture using regression • The model will relate field observed soil water with the backscatter values extracted from the acquired SAR imagery 14 of 21

  15. Project Progress – Analysis/Results Estimation of SAR Based Soil Moisture (Exp. # 2) 15 of 21

  16. Project Progress – Analysis/Results Estimation of SAR Based Soil Moisture (Exp. # 2) 16 of 21

  17. Project Progress – Analysis/Results Multi-temporal Analysis of SAR Data Red: September 19 Green: August 02 Blue: August 26 Estimation of SAR Based Soil Moisture (Exp. # 2) 17 of 21

  18. Upcoming Major Tasks and Issues Tasks • Completion of VIIRS soil moisture estimation and Comparison with MODIS soil moisture • Completion of SAR soil moisture estimation • Generation of VSMS and comparison of VSMS1 and VSMS2 • Evaluate results in the field Issues • Simulation of VIIRS Thermal Data • Acquisition on SAR data (November scene) 18 of 21

  19. Project Schedule – Revised Shows when project officially started and fund distribution started Task planned Task in progress Task completed

  20. Future Prospects • Input of high resolution soil moisture information in DSS • PECAD, SWAT. AGWA • Example: Performance of PECAD can be enhanced by improving crop stress and growth prediction • Currently using- Palmer two layer soil moisture model • Recently projects funded for the inclusion of AMSR-E 25 km soil moisture product • Evaluation of SMAP Mission Performance using Aquarius • Consists of both active and passive microwave sensors for soil moisture mapping • Current project demonstrates the prospects of fusion of active and passive microwave data for producing high resolution soil moisture maps 20 of 21

  21. Thank You!

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