The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain University of Mississippi Geoinformatics Center The University of Mississippi Evaluating Next Generation NASA Earth Science Observations for Image Fusion to Enable Mapping Variation in Soil Moisture at High Resolution Rapid Prototyping Capability for Earth-Sun Systems Sciences

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Purpose and Scope Study Site Potential Decision Support Tools Data Used RPC Experiments Preliminary Results Project Status OUTLINE 3 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 PURPOSE AND SCOPE  Mapping soil moisture at both high spatial and temporal resolution not possible due to lack of sensors with these combined capabilities Mapping soil moisture at high resolution?  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) Virtual Soil Moisture Sensor (VSMS)!  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 Rapid Prototyping Capability (RPC) Project 4 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 MODIS VS. VIIRS 5 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007  Part of Nash Draw in southeastern New Mexico.  Project site is a part of Chihuahuan Desert. Site extent: approximately 400 sq. km. STUDY SITE Location of Nash Draw (Holt et al., 2005)  Semi-arid area  Karst topography 6 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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 37 o 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 DATA USED Image Acquisition Dates 9 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 FORMULATION CHART Prediction and Measurements: Soil moisture at high resolution (10 m/daily) System Model: VI-LST Triangle Model, Regression, ANN, SHEELS, RTM and DisaggNet Earth Observations: MODIS Reflectance MODIS Thermal VIIRS Reflectance VIIRS Thermal AMSR-E Soil Moisture RADARSAT 1 SAR Fine Field Data Decision Support: AWARD SWAT PECAD 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) 10 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENTS  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 Three RPC experiments in the project 11 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENT # 1  Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model MODIS SM(1km) NDVI LST LST: Land Surface Temperature AMSR-E SM R R: Regression

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENT # 1 NDVI LST MODIS AMSR-E SM MODIS SM(1km) R R: Regression LST: Land Surface Temperature  Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI- LST Triangle Model 14 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENT # 1 NDVI LST VIIRS AMSR-E SM VIIRS SM(1km) R R: Regression LST: Land Surface Temperature  Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI- LST Triangle Model 15 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENT # 2  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 Field Data R R: Regression R ANN ANN: Artificial Neural Network SAR SM (10 m) SM: Soil Moisture VSMS1 M SM (10 m) VSMS: Virtual Soil Moisture Sensor

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 RPC EXPERIMENT # 2  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 Field Data R R: Regression R ANN ANN: Artificial Neural Network SAR SM (10 m) SM: Soil Moisture VSMS1 V SM (10 m) VSMS: Virtual Soil Moisture Sensor

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 EVALUATION OF VIIRS TO MODIS  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 RPC Experiment # 1 (Soil Moisture Estimation) 18 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 EVALUATION OF VIIRS TO MODIS  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, VSMS V and NSM M 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 RPC Experiment # 2 (VSMS Generation) 19 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 PRELIMINARY RESULTS  Soil Sample Locations in the Study Site  Samples analyzed for volumetric soil moisture measurements 20 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 PRELIMINARY RESULTS 21 of 25

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 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

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 PROJECT SCHEDULE

The University of Mississippi Geoinformatics Center NASA MRC RPC – 11 July 2007 Thank You! 25 of 25