The satellite microwave remote sensing for precipitation measurement becomes a
key resource for flood forecasting. Global Precipitation Measurement (GPM), a joint
mission of JAXA and NASA, is providing precipitation data since 2014 with higher
spatial (one-tenth of a degree) and temporal (half-hourly) resolution and wider
coverage (65
N to 65
S) compared to its ancestor Tropical Rainfall Measuring
Mission (TRMM). Like microwave remote sensing, optical and thermal remote
sensing are also useful for precipitation estimation. For instance, the visible channel
can be used for cloud thickness estimation; IR can be utilized for raindrop shape and
particle analysis; and thermal IR (TIR) can be useful for cloud top temperature
(Lensky and Rosenfeld 1997). Many near-real-time flood forecasting systems are
utilizing these near-real-time precipitation estimations along with the other model
parameters of the hydrological model. A global flood monitoring system based on
the TRMM multi-satellite precipitation analysis (TMPA) has been developed by
Hong et al. (2007). Though satellite-estimated rainfall data are becoming popular for
flood forecasting due to the increasing availability and cost-effectiveness, these
rainfall estimation data are coarse for precise forecasting at the basin scale.
For accurate flood forecasting, modeling of surface runoff is also as important as
rainfall estimation. DEM, land cover, surface roughness, and soil permeability are
required parameters for the runoff modeling. Depending on the scale, many recent
studies used land cover derived from fine to coarse resolution remote sensing images
(e.g., Landsat and MODIS) in flood modeling. Surface roughness and surface
imperviousness can also be determined from the land cover. The digital elevation
model is one of the most important parameters for hydrological modeling because
without the surface height information, it is almost impossible to provide accurate
flood forecasting. DEM can serve many purposes such as catchment delineation, the
bathymetry of the river, river discharge estimation, as well as river and stream
channel extraction. DEM from Thermal Emission and Reflection Radiometer
(ASTER) and Radar Topography Mission (SRTM) is popular in flood modeling
because of its global coverage and availability at no cost. The preciseness of these
flood models depends on the spatial resolution of DEM; therefore, fine resolution
DEM might be expected. Fine resolution DEM might be available such as LiDARderived DEM, DEM from InSAR data for many parts of the world, but obviously,
the cost is involved. With the advanced Web-based geoinformation system, Webbased flood monitoring systems such as the Global Flood Monitoring System
(GFMS) and Extreme Rainfall Detection System are developed. These flood forecasting systems are primarily dependent on the remote sensing–derived rainfall
measurement, land cover data, and digital elevation model (DEM).
16.2.2.2 Remote Sensing in Flood Mapping
Flood mapping also called flood extent mapping, flood delineation, or inundation
mapping is crucial for flood monitoring, damage assessment, and another aspect of
flood management. Near-real-time flood mapping is very helpful for crop condition
and crop damage assessment. Remote sensing technology allows low-cost, near16 Flood Monitoring and Crop Damage Assessment
325
key resource for flood forecasting. Global Precipitation Measurement (GPM), a joint
mission of JAXA and NASA, is providing precipitation data since 2014 with higher
spatial (one-tenth of a degree) and temporal (half-hourly) resolution and wider
coverage (65
N to 65
S) compared to its ancestor Tropical Rainfall Measuring
Mission (TRMM). Like microwave remote sensing, optical and thermal remote
sensing are also useful for precipitation estimation. For instance, the visible channel
can be used for cloud thickness estimation; IR can be utilized for raindrop shape and
particle analysis; and thermal IR (TIR) can be useful for cloud top temperature
(Lensky and Rosenfeld 1997). Many near-real-time flood forecasting systems are
utilizing these near-real-time precipitation estimations along with the other model
parameters of the hydrological model. A global flood monitoring system based on
the TRMM multi-satellite precipitation analysis (TMPA) has been developed by
Hong et al. (2007). Though satellite-estimated rainfall data are becoming popular for
flood forecasting due to the increasing availability and cost-effectiveness, these
rainfall estimation data are coarse for precise forecasting at the basin scale.
For accurate flood forecasting, modeling of surface runoff is also as important as
rainfall estimation. DEM, land cover, surface roughness, and soil permeability are
required parameters for the runoff modeling. Depending on the scale, many recent
studies used land cover derived from fine to coarse resolution remote sensing images
(e.g., Landsat and MODIS) in flood modeling. Surface roughness and surface
imperviousness can also be determined from the land cover. The digital elevation
model is one of the most important parameters for hydrological modeling because
without the surface height information, it is almost impossible to provide accurate
flood forecasting. DEM can serve many purposes such as catchment delineation, the
bathymetry of the river, river discharge estimation, as well as river and stream
channel extraction. DEM from Thermal Emission and Reflection Radiometer
(ASTER) and Radar Topography Mission (SRTM) is popular in flood modeling
because of its global coverage and availability at no cost. The preciseness of these
flood models depends on the spatial resolution of DEM; therefore, fine resolution
DEM might be expected. Fine resolution DEM might be available such as LiDARderived DEM, DEM from InSAR data for many parts of the world, but obviously,
the cost is involved. With the advanced Web-based geoinformation system, Webbased flood monitoring systems such as the Global Flood Monitoring System
(GFMS) and Extreme Rainfall Detection System are developed. These flood forecasting systems are primarily dependent on the remote sensing–derived rainfall
measurement, land cover data, and digital elevation model (DEM).
16.2.2.2 Remote Sensing in Flood Mapping
Flood mapping also called flood extent mapping, flood delineation, or inundation
mapping is crucial for flood monitoring, damage assessment, and another aspect of
flood management. Near-real-time flood mapping is very helpful for crop condition
and crop damage assessment. Remote sensing technology allows low-cost, near16 Flood Monitoring and Crop Damage Assessment
325