greg easson, ph.d.- ( pi) robert holt, ph.d.- ( co-pi) a. k. m. azad hossain patrick yamnik

The University of Mississippi Geoinformatics CenterNASA MRC RPC: 14-15 April 2008

Greg Easson, Ph.D.- (PI)Robert Holt, Ph.D.- (Co-PI)

A. K. M. Azad HossainPatrick Yamnik

University of Mississippi Geoinformatics CenterThe 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

Robert Ryan, Ph.D. Mr. Kent Hilbert

SSAI-SSC

Don Atwood, Ph.D. Alaska Satellite Facility

Mr. Michael B. HillesheimSandia National

Laboratories

Dennis Powers, Ph.D. Consulting Geologist

Collaborators

Project Team


Project Overview Objectives

Study Site

RPC Experiments

Project Progress Data Collection/Processing

Data Analysis and Results Upcoming Major Tasks Schedule/Current Status Future Prospects

Outline

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The University of Mississippi Geoinformatics CenterNASA MRC RPC: 14-15 April 20083 of 21

Problem Statement

Not possible at both high spatial and temporal resolution due to lack of sensors with these combined capabilities

Mapping Soil Moisture


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)

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Project Overview - Objectives

We designed a RPC project to evaluate potential of VIIRS to replace MODIS to improve monitoring soil moisture by generating VSMS

Rapid Prototyping Capability (RPC) Project


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

Project Overview – Study Site

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Project Overview – 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

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Estimation of MODIS/VIIRS Based Soil Moisture

AMSR-E

MODIS

SM R

MODIS SM(1 km)

NDVI LST

Reflectance Data (MOD09)

Thermal Data (MOD11)

VI- LST Triangle Model

Project Overview – Experiment # 1

VIIRSVIIRSVIIRS

Simulated Reflectance

Simulated Thermal Data

VIIRS SM(800 m)

)()(2

0

2

0jii

i

j

j ij TNDVIaMAMSRE

)()(2

0

2

0jii

i

j

j ij TNDVIaMAMSRE

)()(2

0

2

0jii

i

j

j ij TNDVIaMMODIS

)()(2

0

2

0jii

i

j

j ij TNDVIaMMODIS

NDVI-Normalized Difference Vegetation Index LST-Land Surface Temperature

R- Regression

VI-Vegetation Index

SM-Soil Moisture

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Field Data

MODISSM(1 km)

R

SAR-Synthetic Aperture Radar

SM-Soil Moisture

ANN-Artificial Neural Network

R-Regression

VSMS1-Virtual Soil Moisture Sensor

M-MODIS

V-VIIRS

RADARSAT 1

SAR Fine Imagery(10 m)

SARSM(10 m)

R

ANN

VSMS1M

SM(10 m)

VIIRSSM (800 m)

VSMS1V

SM(10 m)

Generation of Virtual Soil Moisture Sensor 1 (VSMS1)


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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

VSMS2 (M)

SM(10 m)

DisaggNet

RTM

SHEELS

MODIS SM(1 km)

SM

Emissivity VIIRS

SM (800 m)

VSMS2 (V)

SM(10 m)

SM - Soil Moisture

VSMS2 - Virtual Soil Moisture Sensor 2

M - MODIS

V - VIIRS


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Data Collection and Processing

Project Progress - Data

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R

AMSR-E L3 25 km SM

MOD11 1km FLST

MOD09 250 m FNDVI

VI-LST Triangle Model 1 km SM

MODIS/VIIRS Based Soil Moisture Estimation (Exp. # 1)Project Progress – Analysis/Results

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MODIS/VIIRS Based Soil Moisture Estimation (Exp.#1)

AMSR-E Soil Moisture (25 km resolution)

7.6 7.7

6.6 6.9

Project Progress – Analysis/Results

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Project Progress – Analysis/Results Comparison between VIIRS and MODIS NDVI (EXP. # 1)

R2 = 0.47

R2 = 0.60

R2 = 0.53

R2 = 0.17

R2 = 0.71

R2 = 0.68

R2 = 0.71

R2 = 0.71

R2 = 0.66

R2 = 0.45

R2 = 0.66

R2 = 0.66

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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

Project Progress – Analysis/Results

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

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Multi-temporal Analysis of SAR Data

Red: September 19

Green: August 02

Blue: August 26


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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)

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Project Schedule – Revised

Task in progressTask planned Shows when project officially started and fund distribution startedTask completed


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

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Thank You!

greg easson, ph.d.- ( pi) robert holt, ph.d.- ( co-pi) a. k. m. azad hossain patrick yamnik

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