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[ACG37-P07] Estimation and Validation of Surface Water Using Multiple Microwave Radiometers
Keywords:surface water , microwave radiometer, flood
Floods, which associated with extreme heavy rainfall, are expected to become more severe with global warming. It is necessary to promptly provide information on flood inundation to help assess the damage and take measures for recovery. Satellite remote sensing plays a very important role because it can provide a bird's eye view of the situation from space even in inaccessible areas during a disaster.
Spaceborne synthetic aperture radar (SAR) can accurately estimate flooded areas based on the difference in reflectivity between water surface areas and others, even in bad weather conditions. For example, Advanced Land Observing Satellite 2(ALOS-2) operated by JAXA is equipped with L-band SAR, which has been used to assess the flood situation through the framework of Sentinel Asia and the International Disasters Charter, but it is difficult to provide observation information frequently especially when there are competing observation requests. Surface water products using visible and infrared sensors are also being developed in multiple organizations, but it is difficult to monitor the situation below the cloud area, making it difficult to provide information promptly during heavy rainfall events. On the other hand, spaceborne microwave radiometers are operated by many organizations in various countries and are not heavily affected by weather conditions, making it possible to monitor global water surface at a certain frequency.
In the previous studies, Takeuchi et al. (2009) calculated the Normalized Differential Frequency Index (NDFI) from the observed brightness temperature of the AMSR-E microwave radiometer onboard the Aqua satellite and used it as an indicator of surface water. Seto et al. (2018) applied the method to AMSR2 and GMI, microwave radiometers operated under the Global Precipitation Measurement (GPM) project, and constructed the Global Satellite Mapping of Wet Surface (GSMaWS) for the five-year period from 2013 to 2017 on a daily and 0.1° grid.
In this study, surface water data from these sensors were generated through 2022, and similar estimates were made using brightness temperatures from the SSMI/S sensors onboard the DMSP-F16, F17, and F18 satellites. The surface water data estimated by each sensor was compared with the flooded fraction(fldfrc) estimated by Today's Earth, a global terrestrial hydrological simulation system developed and operated by JAXA and the University of Tokyo, on a monthly basis over a long period of time. The results showed that the water surface estimated by the microwave imagers are generally captured seasonal variations well in the lower reaches of major rivers, but underestimated them in forested areas where the water-management signals were difficult to detect, as pointed out by Seto et al. 2018. The SSMI/S showed different variations compared to other sensors due to its rough resolution and slightly different frequency used for NDFI calculation. Applications that take advantage of its frequent observation and long-term data set availability should be considered in the future.
Spaceborne synthetic aperture radar (SAR) can accurately estimate flooded areas based on the difference in reflectivity between water surface areas and others, even in bad weather conditions. For example, Advanced Land Observing Satellite 2(ALOS-2) operated by JAXA is equipped with L-band SAR, which has been used to assess the flood situation through the framework of Sentinel Asia and the International Disasters Charter, but it is difficult to provide observation information frequently especially when there are competing observation requests. Surface water products using visible and infrared sensors are also being developed in multiple organizations, but it is difficult to monitor the situation below the cloud area, making it difficult to provide information promptly during heavy rainfall events. On the other hand, spaceborne microwave radiometers are operated by many organizations in various countries and are not heavily affected by weather conditions, making it possible to monitor global water surface at a certain frequency.
In the previous studies, Takeuchi et al. (2009) calculated the Normalized Differential Frequency Index (NDFI) from the observed brightness temperature of the AMSR-E microwave radiometer onboard the Aqua satellite and used it as an indicator of surface water. Seto et al. (2018) applied the method to AMSR2 and GMI, microwave radiometers operated under the Global Precipitation Measurement (GPM) project, and constructed the Global Satellite Mapping of Wet Surface (GSMaWS) for the five-year period from 2013 to 2017 on a daily and 0.1° grid.
In this study, surface water data from these sensors were generated through 2022, and similar estimates were made using brightness temperatures from the SSMI/S sensors onboard the DMSP-F16, F17, and F18 satellites. The surface water data estimated by each sensor was compared with the flooded fraction(fldfrc) estimated by Today's Earth, a global terrestrial hydrological simulation system developed and operated by JAXA and the University of Tokyo, on a monthly basis over a long period of time. The results showed that the water surface estimated by the microwave imagers are generally captured seasonal variations well in the lower reaches of major rivers, but underestimated them in forested areas where the water-management signals were difficult to detect, as pointed out by Seto et al. 2018. The SSMI/S showed different variations compared to other sensors due to its rough resolution and slightly different frequency used for NDFI calculation. Applications that take advantage of its frequent observation and long-term data set availability should be considered in the future.