Assessing global surface water inundation dynamics using combined satellite information from SMAP, AMSR2 and Landsat

A method to assess global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fw_LBand ) retrievals were derived using SMAP H-polarization brightness temperature (T_b ) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency T_b observations from AMSR2. The resulting fw_LBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fw_LBand annual averages corresponded favourably (R=0.85, p-value<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fw_LBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fw_LBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fw_LBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fw_LBand retrievals showed favourable spatial accuracy for water (commission error 31.46%, omission error 30.20%) and land (commission error 0.87%, omission error 0.96%) classifications and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fw_LBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics and potential flood risk.