greg easson, ph.d. robert holt, ph.d. a. k. m. azad hossain

The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007

Greg Easson, Ph.D. Robert Holt, Ph.D.

A. K. M. Azad HossainUniversity 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


PROJECT TEAM

The University of MississippiGreg Easson, PhD Robert Holt, PhDA. K. M. Azad Hossain

Stennis Team

Robert Ryan, Ph.D.

Alaska Satellite Facility Don Atwood, Ph.D.

Sandia National LaboratoriesMr. Michael B. Hillesheim

Consulting GeologistDennis Powers, Ph.D.

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Purpose and Scope

Study Site

Potential Decision Support Tools

Data Used

RPC Experiments

Preliminary Results

Project Status

OUTLINE

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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

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MODIS VS. VIIRS

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Part of Nash Draw in southeastern New Mexico.

Project site is a part of Chihuahuan Desert. Site extent: approximately 400 sq. km.

STUDY SITE

Study Site

Location of Nash Draw (Holt et al., 2005)

Semi-arid area

Karst topography

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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)

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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

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DATA USED

Image Acquisition Dates

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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:

AWARDSWAT

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)

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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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RPC EXPERIMENT # 1 Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation

using VI-LST Triangle Model

MODIS

MODISSM(1km)

NDVI

LST

LST: Land Surface Temperature

AMSR-ESM

R

R: Regression


RPC EXPERIMENT # 1

)2.......()........./()( 00 NDVINDVINDVINDVINDVI s

)3..(..............................)........./()( 00 TTTTT s

)1.....(....................)()(1

01

1

0

jii j

j ij TNDVIaM

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

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RPC EXPERIMENT # 1

)4........(....................)()(1

01

1

0

jM

iM

i j

j ijEAMSR TNDVIaM

)5...(..............................)()(1

01

1

0

jM

iM

i j

j ijM TNDVIaM

NDVI

LST

MODIS

AMSR-ESM

MODISSM(1km)

R

R: Regression


Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model

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RPC EXPERIMENT # 1

)4........(....................)()(1

01

1

0

jV

iV

i j

j ijEAMSR TNDVIaM

)5...(..............................)()(1

01

1

0

jV

iV

i j

j ijV TNDVIaM

NDVI

LST

VIIRS

AMSR-ESM

VIIRSSM(1km)

R

R: Regression


Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model

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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

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Field Data

R

R: Regression

R

ANN

ANN: Artificial Neural Network

SARSM (10 m)

SM: Soil Moisture

VSMS1M

SM (10 m) VSMS: Virtual Soil Moisture Sensor


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

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Field Data

R

R: Regression

R

ANN

ANN: Artificial Neural Network

SARSM (10 m)

SM: Soil Moisture

VSMS1V

SM (10 m) VSMS: Virtual Soil Moisture Sensor


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

)8..(........................................22 PAU

)9...(........................................TA

)10.......(....................)(1

1

1

2

N

iiXN

P

RPC Experiment # 1 (Soil Moisture Estimation)

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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.

)11.(..............................)(1

M

MV

VSMS

VSMSVSMS

nMAPE

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

RPC Experiment # 2 (VSMS Generation)

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PRELIMINARY RESULTS

Soil Sample Locations in the Study Site

Samples analyzed for volumetric soil moisture measurements

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PRELIMINARY RESULTS

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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

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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

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PROJECT SCHEDULE


Thank You!

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greg easson, ph.d. robert holt, ph.d. a. k. m. azad hossain

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