ETWatch:a method of multi-resolution ET data fusion

Abstract

Construction of a high-resolution remote-sensing evapotranspiration (ET) dataset is restricted by remote sensing data sources and clouds. Single remote sensor data cannot cover the land with high spatial and temporal resolution. In this paper, we analyzed the spatial characteristics of different scale ET data in ETWatch, compared several common fusion methods, and analyzed the data characteristics and information before and after data fusion. We integrated the spatial and temporal adaptive reflectance fusion model (STARFM) into ETWatch to fuse different scale remote sensing ET data. The results show that the STARFM fusion method effectively can integrate the spatial and temporal distribution information of high & low resolution data, with an average error of 1.75%, compared with input of 1 km daily ET, with a monthly average error of 0.2% compared with input of 1km month ET. The STARFM model is adaptive to fusing different scales of ET data.