Blending Modis and AMSR2 to Predict Daily Global Inundation Map in 1km Resolution - Takeuchi, Wataru; Youngjoo, Kwak (IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018)
In cloudy area of the Earth, MODIS limits the sensor's ability to quantify biophysical processes in heterogeneous landscape. A passive microwave sensor AMSR2 is not subject to cloud contamination although its spatial resolution is relatively coarse. In this paper, a new spatial and temporal adaptive data fusion model algorithm is presented and demonstrated to blend MODIS and AMSR2 to predict daily land surface water coverage. The MODIS 8 day composite 1km normalized difference water index (NDWI) and AMSR2 daily 16km normalized difference frequency index (NDFI) are used to map land surface water coverage (LSWC) which is effective to monitor agriculture and flood monitoring issues. It was found that the algorithm accurately predicts daily LSWC of AMSR2 at an effective fractional coverage close to that of MODIS.