1 / 21

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

rio
Télécharger la présentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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!

More Related