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Role of GIS in soil moisture modeling and mapping
GIS offers a unique way to integrate point data and remotely sensed data along with other soil sand land use data to provide meaningful and accurate maps of soil moisture,
a critical variable for water resources studies. Such an integration was initially undertaken by Entekhabi and Eagleson (1989). They accounted for variability within the grid cells of a general circulation model using probability distributions of precipitation and soil moisture. Ludwig and Mauser (2000) used the physically based soil–vegetation–atmosphere transfer (SVAT) model PROMET (process-oriented model for evapotranspiration) linked to the SWAT model (incorporating soil physical and plant physiological parameters) within a GIS-based model framework to provide a hydrological model covering the water cycle at the basin scale at a 30m resolution. Strasser and Mauser (2001) used the physically based SVAT model PROMET to analyze the spatial and temporal variations of the water balance components in a 4D GIS data
structure with data inputs including DEMs and soil texture information derived from digitized maps, land use distribution from NOAA/AVHRR satellite images, and meteorological data. Habets and Saulnier (2001) proposed a methodology to quantify surplus and runoff on a subgrid parameterization, using the TOPMODEL hydrological framework.
Hirabayashi et al. (2003) presented a simple algorithm for transferring root-zone soil moisture from surface soil moisture data on a global scale. Singh et al. (2004) incorporated the water balance approach using the Thornthwaite and Mather (TM) model combined with remote sensing and GIS to
determine the periods of moisture deficit and moisture surplus for an entire basin.
Moran et al. (2004) used remote sensing techniques and land surface models (SVAT) to estimate soil moisture with known accuracy at the watershed scale. In recent years, several studies have improved the quantification of the hydrologic budget and the prediction of soil moisture at local and regional scales using remotely sensed data, but these studies are at coarse spatial and temporal resolutions (e.g., Becker 2006; Tweed et al. 2007; and others).
The spatial resolution of space-borne satellites is, at best, ±30 m for Landsat ETM. The recently launched Soil Moisture and Ocean Salinity Satellite (SMOS), a part of ESA's Living Planet Programme, will monitor surface soil moisture at 35 km spatial resolution with a time step of 2–3 days (European Space Agency 2010). The Hydrosphere State Mission (Hydros), a pathfinder mission in the NASA's Earth System Science Pathfinder Program (ESSP), provides exploratory global measurements of the earth's soil moisture at 10 km resolution with a 2-day to 3-day revisit. Newer studies are adopting a hybrid methodology to combine site-specific data (near real time) and mass balance methods in a GIS to create spatially explicit soil moisture maps at higher spatial and temporal scales (Connelly 2010).
GIS offers a unique way to integrate point data and remotely sensed data along with other soil sand land use data to provide meaningful and accurate maps of soil moisture,
a critical variable for water resources studies. Such an integration was initially undertaken by Entekhabi and Eagleson (1989). They accounted for variability within the grid cells of a general circulation model using probability distributions of precipitation and soil moisture. Ludwig and Mauser (2000) used the physically based soil–vegetation–atmosphere transfer (SVAT) model PROMET (process-oriented model for evapotranspiration) linked to the SWAT model (incorporating soil physical and plant physiological parameters) within a GIS-based model framework to provide a hydrological model covering the water cycle at the basin scale at a 30m resolution. Strasser and Mauser (2001) used the physically based SVAT model PROMET to analyze the spatial and temporal variations of the water balance components in a 4D GIS data
structure with data inputs including DEMs and soil texture information derived from digitized maps, land use distribution from NOAA/AVHRR satellite images, and meteorological data. Habets and Saulnier (2001) proposed a methodology to quantify surplus and runoff on a subgrid parameterization, using the TOPMODEL hydrological framework.
Hirabayashi et al. (2003) presented a simple algorithm for transferring root-zone soil moisture from surface soil moisture data on a global scale. Singh et al. (2004) incorporated the water balance approach using the Thornthwaite and Mather (TM) model combined with remote sensing and GIS to
determine the periods of moisture deficit and moisture surplus for an entire basin.
Moran et al. (2004) used remote sensing techniques and land surface models (SVAT) to estimate soil moisture with known accuracy at the watershed scale. In recent years, several studies have improved the quantification of the hydrologic budget and the prediction of soil moisture at local and regional scales using remotely sensed data, but these studies are at coarse spatial and temporal resolutions (e.g., Becker 2006; Tweed et al. 2007; and others).
The spatial resolution of space-borne satellites is, at best, ±30 m for Landsat ETM. The recently launched Soil Moisture and Ocean Salinity Satellite (SMOS), a part of ESA's Living Planet Programme, will monitor surface soil moisture at 35 km spatial resolution with a time step of 2–3 days (European Space Agency 2010). The Hydrosphere State Mission (Hydros), a pathfinder mission in the NASA's Earth System Science Pathfinder Program (ESSP), provides exploratory global measurements of the earth's soil moisture at 10 km resolution with a 2-day to 3-day revisit. Newer studies are adopting a hybrid methodology to combine site-specific data (near real time) and mass balance methods in a GIS to create spatially explicit soil moisture maps at higher spatial and temporal scales (Connelly 2010).