The combined use of satellite data from multispectral and TIR radiometers has
also shown promise for the retrieval of latent and sensible heat, as well as surface
soil moisture variations. These data are important for monitoring plant growth and
productivity, irrigation management, modeling atmospheric and hydrological
cycles, and improving the accuracy of weather forecast models.
Blending optical data, passive microwave emissivity, and radar backscatter, the
Global Inundation Extent from Multi-Satellite (GIEMS) data set is one of the most
comprehensive data sets of surface wetness [12, 13]. GIEMS is a coarse scale data
set with about 25 km spatial resolution and monthly time-steps available for almost
two decades. Recently both high-resolution SAR data [42] and optical images from
the MODIS imagery [43] have been used for downscaling GIEMS.
Detecting water in arid regions by means of soil moisture remote sensing still
faces many challenges. The spatial resolution over land needs to be improved. As
stated earlier, accurate soil moisture estimates are limited to regions that have either
bare soils or low amounts of vegetation cover. Better corrections for surface
roughness, vegetation cover, soil temperature, and topography must be devised.
Until recently, the moisture in only the top few centimeters of the soil could be
detected. The Soil Moisture Active Passive (SMAP) satellite mission, planned for
the 2015–2020 time frame, is designed to use advanced modeling and data assimilation to provide data on deeper root-zone soil moisture and net ecosystem
exchange of carbon [44, 45].
4.2 Detecting Groundwater
Groundwater accounts for about 98 % of the total freshwater budget on Earth. The
remaining 2 % is divided between rivers, lakes, freshwater wetlands, and moisture
in the atmosphere. Groundwater supplies about 40 % of the drinking water in the
USA and 70 % in China and is the main source of domestic water supply in most
European countries [46]. Groundwater exists within the matrix of sedimentary
rocks, occupying pore spaces between sediment grains, housed within rock fractures, or held within large underground caverns [10, 47].
Shallow-layer groundwater within a few centimeters of the surface may be
detected by SAR microwave radiometry or TIR imagery in certain geologic settings
[48]. However, groundwater aquifers located deep below the land surface cannot be
detected directly by electromagnetic remote sensors and are usually surveyed with
gravitational techniques. Groundwater discharges to the surface via springs that
feed rivers, lakes, and wetlands and can be mapped by many different remote
sensing systems, as described in later sections of this chapter. In arid environments,
the surface discharge of groundwater may also be indirectly detected by remote
sensing, if the discharge produces areas of unusually dense vegetation [49].
High-resolution gravitational surveys have been used to estimate groundwater
storage [50, 51]. However, gravitational instruments have no vertical resolving
power, so that measurement of subsurface water pools requires removing the
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
41
also shown promise for the retrieval of latent and sensible heat, as well as surface
soil moisture variations. These data are important for monitoring plant growth and
productivity, irrigation management, modeling atmospheric and hydrological
cycles, and improving the accuracy of weather forecast models.
Blending optical data, passive microwave emissivity, and radar backscatter, the
Global Inundation Extent from Multi-Satellite (GIEMS) data set is one of the most
comprehensive data sets of surface wetness [12, 13]. GIEMS is a coarse scale data
set with about 25 km spatial resolution and monthly time-steps available for almost
two decades. Recently both high-resolution SAR data [42] and optical images from
the MODIS imagery [43] have been used for downscaling GIEMS.
Detecting water in arid regions by means of soil moisture remote sensing still
faces many challenges. The spatial resolution over land needs to be improved. As
stated earlier, accurate soil moisture estimates are limited to regions that have either
bare soils or low amounts of vegetation cover. Better corrections for surface
roughness, vegetation cover, soil temperature, and topography must be devised.
Until recently, the moisture in only the top few centimeters of the soil could be
detected. The Soil Moisture Active Passive (SMAP) satellite mission, planned for
the 2015–2020 time frame, is designed to use advanced modeling and data assimilation to provide data on deeper root-zone soil moisture and net ecosystem
exchange of carbon [44, 45].
4.2 Detecting Groundwater
Groundwater accounts for about 98 % of the total freshwater budget on Earth. The
remaining 2 % is divided between rivers, lakes, freshwater wetlands, and moisture
in the atmosphere. Groundwater supplies about 40 % of the drinking water in the
USA and 70 % in China and is the main source of domestic water supply in most
European countries [46]. Groundwater exists within the matrix of sedimentary
rocks, occupying pore spaces between sediment grains, housed within rock fractures, or held within large underground caverns [10, 47].
Shallow-layer groundwater within a few centimeters of the surface may be
detected by SAR microwave radiometry or TIR imagery in certain geologic settings
[48]. However, groundwater aquifers located deep below the land surface cannot be
detected directly by electromagnetic remote sensors and are usually surveyed with
gravitational techniques. Groundwater discharges to the surface via springs that
feed rivers, lakes, and wetlands and can be mapped by many different remote
sensing systems, as described in later sections of this chapter. In arid environments,
the surface discharge of groundwater may also be indirectly detected by remote
sensing, if the discharge produces areas of unusually dense vegetation [49].
High-resolution gravitational surveys have been used to estimate groundwater
storage [50, 51]. However, gravitational instruments have no vertical resolving
power, so that measurement of subsurface water pools requires removing the
Using Remote Sensing to Map and Monitor Water Resources in Arid and Semiarid. . .
41