Algorithm of Flood Monitoring and Analysis based on SMAP Data

Abstract

The method of flood monitoring based on Soil Moisture Active Passive (SMAP) radiometer data is put forward. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution is higher than others passive microwave data, such as AMSR-E, ATMS and SSM/I. In order to study the SMAP’s potential ability, the case of flood induced by Hurricane Harvey occurred in 2017 in United State was selected to apply the SMAP data to monitor the inundated water. The result showed that the flood area can be reflected based on SWAP data, and H polarization data is more sensitive to the V polarization data. The mixed pixel separation method is applied to estimate the water fraction and provide more detailed sub-pixel scale water information. The SMAP L1C enhanced brightness temperature product with 9 km spatial resolution can provide more detailed flood information than previous passive microwave data. By compare the result with Fengyun-3 Microwave Radiation Imager (FY-3/MWRI) data and NASA's Earth Observing System Moderate Resolution Imaging Spectroradiometer (EOS/MODIS) data, there are better accordance with the SMAP data, which prove the effect of SMAP data for flood monitoring.