Greg Easson, H. G. Momm The University of Mississippi Ronald Bingner
Greg Easson, Ph.D. Robert Holt, Ph.D. A. K. M. Azad Hossain
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Transcript of 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
The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
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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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
Purpose and Scope
Study Site
Potential Decision Support Tools
Data Used
RPC Experiments
Preliminary Results
Project Status
OUTLINE
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The University of Mississippi Geoinformatics CenterNASA 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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
MODIS VS. VIIRS
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The University of Mississippi Geoinformatics CenterNASA 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
Study Site
Location of Nash Draw (Holt et al., 2005)
Semi-arid area
Karst topography
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The University of Mississippi Geoinformatics CenterNASA 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)
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The University of Mississippi Geoinformatics CenterNASA 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 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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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
DATA USED
Image Acquisition Dates
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The University of Mississippi Geoinformatics CenterNASA 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:
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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The University of Mississippi Geoinformatics CenterNASA 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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
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
The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
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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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
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
LST: Land Surface Temperature
Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model
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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
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
LST: Land Surface Temperature
Goal: Evaluate VIIRS to replace MODIS in Soil Moisture estimation using VI-LST Triangle Model
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The University of Mississippi Geoinformatics CenterNASA 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
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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
The University of Mississippi Geoinformatics CenterNASA 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
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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
The University of Mississippi Geoinformatics CenterNASA 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
)8..(........................................22 PAU
)9...(........................................TA
)10.......(....................)(1
1
1
2
N
iiXN
P
RPC Experiment # 1 (Soil Moisture Estimation)
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The University of Mississippi Geoinformatics CenterNASA 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.
)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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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
PRELIMINARY RESULTS
Soil Sample Locations in the Study Site
Samples analyzed for volumetric soil moisture measurements
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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
PRELIMINARY RESULTS
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The University of Mississippi Geoinformatics CenterNASA 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
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The University of Mississippi Geoinformatics CenterNASA 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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
PROJECT SCHEDULE
The University of Mississippi Geoinformatics CenterNASA MRC RPC – 11 July 2007
Thank You!
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