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Integrating Satellite Remote Sensing and In-situ Measurements for Monitoring Surface Soil Moisture
John J. Qu, Xianjun Hao, Rui Zhang, and Ray MothaGlobal Environment and Natural Resources Institute (GENRI)
George Mason University, Fairfax, VA 22030, [email protected]
Robert Stefanski Agricultural Meteorology Division, World Meteorological Organization
Geneva, SWITZERLANDOrivaldo Brunini
Agronomic Institute, BRAZILJohan Malherbe
ARC, LNRPretora, SOUTH AFRICA
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Outline
Introduction
Soil Moisture Moisture Monitoring with MODIS
Soil Moisture- Remotely Monitoring and Analysis System (SM-RMAS)
Integrated SMOS and MODIS SM data
Africa Soil Moisture Project
Summary and Discussions
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Overview of Soil Moisture Monitoring
Soil moisture is defined as the amount of water in soil.
Soil moisture is an important factor for agricultural drought.
Soil moisture is one of important climate change indicators.
Soil moisture is a critical parameter for IPCC and GFCS.
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Overview of Soil Moisture Measurements
In-situ ground measurements Point measurements
Restricted to discrete observation
SM measurement standards and guidelines
Satellite remote sensing Provide large scale and long term observation
Poor spatial resolution for Microwave
Lack of physical basis for Optical/IR
Vegetation cover makes it complex
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Soil Moisture Monitoring with MODIS
Combining MODIS and ground measurements for soil moisture estimation Wang et al., 2007a. International Journal of Remote Sensing
Using multiple NIR-SWIR channels to estimate soil and vegetation moisture Wang et al., 2007b. International Journal of Remote Sensing Wang and Qu, 2007. GRL
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>11 years (2003 ~ present) data from 137 ground stations 10 cm, 20 cm and 40 cm soil moisture at 8 AM on every 6th, 16th, and 26th
Ground Measurements (~100km2/station)
Data:
MODIS NDVI and LST: 1 km resolution
Remote sensing
Surface cover and soil type 1 km resolution
Soil Moisture Estimation Using MODIS and In-situ Measurements
Study Area: Shandong, China
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Soil Moisture Estimation Using MODIS and Ground Measurements
T*
NDVI* Low soil moisture
High soilmoisture
NDVIo
NDVIs
To Ts
Universal Triangle (Semi-Physical)(Carlson et al. 1994, Chauhan et al. 2003)
)*(
0
)*(
0
jni
i
inj
jji TNDVIa
os
o
TTTT
T
*
os
o
NDVINDVINDVINDVI
NDVI
*
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θ: soil moisture
(1)
(2)
(3)
The vegetation T is always close to air T, the spatial variation in surface T is small (except for emission from underlying bare soil) over a full vegetation. The surface T over bare soil varies depending on θ: from warm to cold when θ increases.
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Soil Moisture Estimation Using MODIS and Ground Measurements
Regression Model
)*(2
0
)*(2
0
ji
i
ij
jji TNDVIa
Soil Moisture Estimation Algorithm
Soil Moisture from Ground Observations
Regression Algorithm
MODIS NDVI, LST
Soil Moisture at MODIS Resolution
θ=F(NDVI,LST)Regression Coefficients
MODIS Soil Moisture Product Flowchart
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Soil moisture product at MODIS resolution
Soil Moisture Estimation Using MODIS and Ground Measurements
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The successful application of ‘Universal Triangle’ relation reinforced this theory;
A good agreement between ground observed and MODIS derived soil moisture suggested soil moisture retrieval by combining in-situ and MODIS measurements is feasible;
The soil moisture map at 1 km resolution provides more regional soil moisture details and spatial pattern.
Soil Moisture Estimation Using MODIS and Ground Measurements
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Moisture effect on canopy reflectance
A new drought index (NMDI) for monitoring soil and vegetation moisture
Integration of multi-space sensor, such as SMOS, SMAP and MODIS and in-situ soil moisture measurements
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Soil Moisture Estimation Using Multiple Sensor Measurements
SWIR Bands are
0
0.2
0.4
0.6
0.8
1
400 800 1200 1600 2000
Wavelength (nm)
U.S. 1976 Standard Atmosphere
Atm
osph
eric
Tra
nsm
ittan
ce
SWIR Bands
13Credit to Dr. Menghua Wang NOAA/STAR
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Limitations:
Cannot remove soil effects Very few similar studies focused on soil moisture
NIR-SWIR vegetation water indices:
NDWI, NDII, WI, GVWI…
Soil Moisture Estimation Using Multiple MODIS SRB Measurements
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Moisture effect on canopy reflectance
PROSPECT
SAIL
Leaf Reflectance
Soil Model
Soil Reflectance
Canopy Reflectance
Coupled soil-leaf-canopy model
(Jacquemund 1990)
(Verhoef 1984)
(Lobell 2002)
Soil Moisture Estimation Using Multiple MODIS SRB Measurements
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NMDI (Normalized Multi-band Drought Index) for remotely sensing soil and vegetation moisture
Formation of NMDI
)()(
21301640860
21301640860
nmnmnm
nmnmnm
RRRRRRNMDI
Simulated soil spectra at various θ and canopy spectra at different Cw
θ R1640nm-R2130nm
Cw (cm)
0
5
10
15
20
25
30
35
500 900 1300 1700 2100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
0.008
0.016
0.024
0.032
0.04
N=1.3Cab=40μg/cm2
Cm=0.1g/cm2
LAI=2
θ=25%
Cw
R1640nm-R2130nm
θ: soil moisture; Cw: leaf water content
Cw (cm)
0
5
10
15
20
25
30
35
500 900 1300 1700 2100
Wavelength [nm]
Spec
tral R
efle
ctan
ce [%
]
0.008
0.016
0.024
0.032
0.04
N=1.3Cab=40μg/cm2
Cm=0.1g/cm2
LAI=2
θ=25%
Cw
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NMDI turns to be a vegetation water index at LAI≥2.
NMDI increases almost linearly with Cw, presenting parallel trends for each LAI.
Sensitivity of NMDI to vegetation areas
LAI
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 0.01 0.02 0.03 0.04 0.05
Cw(cm-1)
NM
DI
0.5123456
R1640nm-R2130nm
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Cw: leaf water content; LAI: leaf area index
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0.5
0.6
0.7
0.8
0.9
1
0 0.03 0.06 0.09 0.12 0.15 0.18 0.21Soil Moisture (Vol)
NM
DI
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0 0.2 0.4 0.6Soil Moisture (Vol)
NM
DI
Higher NMDI, drier soil. θ <0.1,NMDI = 0.7~1; θ=0.2, NMDI≈ 0.6; θ >0.5, NMDI<0.4. Suggest that soil moisture conditions can be monitored by NMDI with different thresholds.
Validation: MODIS Soil Moisture Products
θ: soil moisture
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NMDI is well suited to estimate both soil and vegetation drought.
NMDI demonstrated high performance and discrimination power for fire detection.
The new index will provide a new foundation for monitoring soil moisture for the next generation of MODIS sensor –VIIRS.
Soil Moisture Estimation Using Multiple MODIS SRB Measurements
SM-RMAS (Soil Moisture- Remotely MonitoringMonitoring and Analysis is a near real time operational system including: (1) satellite remote sensing data processing sub-system, (2) ground measurement database; (3), SM computing sub-system, (4) SM prediction sub-system, and (5) SM analysis and DSS sub-system.
Africa Soil Moisture Project
Agricultural Extension
.
GMU, USDA & UFl Crop Modeling & DSS Tools
for Data Management
Decision-Making
Kenya & Other African Nations
Satellite Remote Sensing Data
Soil & Crop Moisture
Data Products
Decision SupportSystem
User Community
Policy Making
Farm Decisions
Rain GaugeOn Site
Data
IBIMETSNU/NCAM
WAMIS
WC&F Seminars
andTraining
National Drought Policy
Drought/Flood/Heat
Drought/Flood/Heat
Integrated GIS
AgrometData Products
Extension &Training
Satellite Remote Sensing
Decision Support
Soil/Vegetation Moisture Estimation
Drought Assessment
Ground Measurements
Africa SM System Infrastructure
http://wamis.gmu.edu/Africa/
South Africa SM Project Workshop
In-situ Soil Moisture Monitoring Sites in South Africa
Soil Moisture Monitoring in South Africa
Soil Moisture Measurements from SMOS/MODIS
Vegetation Condition Index Temperature Condition Index
Drought Severity Index Evapotranspiration
Soil Moisture Monitoring in Brazil
Soil Moisture Measurements from SMOS/MODIS
2014/4/928Soil Moisture Measurements from SMOS/MODIS
Soil Moisture Monitoring in Mozambique
Soil Moisture Processing System Infrastructure
Satellite & Ground Measurements
Remote Sensing data
SurfaceInformation
Ground Measurements
Data Processing System
Real‐time RSData Processing
Level 2 Data Production
RS DataAnalysis
Inversion Model Cal/Val
Moisture Content Estimation
Data Products
Soil Moisture
Vegetation Moisture
Drought Index
Drought Rating
Application and Decision Support
Drought Assessment
Drought Forecasting
Decision Support
Africa SM Data Dissemination
Public web site Provide GIS based web services to public users and
agencies Dedicated data transfer protocols For institutions and agencies to get data for decision
support and further analysis Mobile applications For public users to view information on mobile devices
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Summary and Discussion
Soil moisture estimation by combining the strengths of SMOS, SMAP, MODIS and ground measurements to achieve higher accuracy and spatial resolution.
System (SM-RMAS) is under development. We need to work with our international partners to collect more ground measurements.
We have tested our approach with SMOS and MODIS & in-situ measurements applying it in multiple regions, including China, South Africa, Brazil, and Mozambique.
There is a urgent need to develop standards and guidelines for global soil moisture measurements.
Thank You !
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Di Wu, 2014, An Investigation of Agricultural Drought on the United State Corn Belt Using Satellite Remote Sensing and GIS Technology, GMU Ph.D Thesis
Wang, Lingli, 2008, Remote Sensing Techniques for Soil Moisture and Agricultural Drought Monitoring, GMU Ph.D. Thesis.
Dasgupta, Swarvanu, 2007, Remote Sensing Techniques for Vegetation Moisture and Fire Risk Estimation, GMU Ph.D. Thesis.
Hao, Xianjun, 2006, Estimation of Live Fuel Moisture and Soil Moisture Using Satellite Remote Sensing, GMU Ph.D. Thesis.
Soriano, Melissa, 2008, Estimation of Soil Moisture in the Southern United States in 2003 Using Multi-Satellite Remote Sensing Measurements. GMU, Master Thesis
Li, Min 2010, Remote Sensing Techniques for Detecting Vegetation Phenology, GMU Ph.D. Thesis.
Wang, Wanting, 2009, Satellite Remote Sensing of Forest Disturbances Caused by Hurricanes and Wildland Fires. GMU Ph.D. Thesis.
Xie, Yong, 2009, Detection of Smoke and Dust Aerosols Using Multi-sensor Satellite Remote Sensing Measurements. GMU Ph.D. Thesis
Related Publications
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Acknowledgments
This project has been funded by WMO and UCAR, NOAA/NWS.
We appreciate the supporting from our GENRI/ESTC partners, WMO, NWS/WR, USDA/FS/SRS, UADA/WAOB, USDA/ARS and international partners, such as ARC, South Africa and Agronomic Institute, Brazil.