Monitoring of "All-weather" Evapotranspiration Using Multi-source Remote Sensing Imagery Over the River Source Region in Southwest China

Accurate estimation of surface evapotranspiration (ET) with high quality and fine spatiotemporal resolution is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale．Integrating multi-source remote sensing data is one of the main ideas for many scholars to obtain synthesized frequent high spatial resolution surface ET. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces, etc. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism needs further investigation, including the applicability and the influencing factors, such as local environment, heterogeneity of the landscape, and optimized parametric scheme, for improving estimation accuracy. In addition, due to technical and budget limitations, so far, no single sensor provides both high spatial resolution and high temporal resolution. Optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions. The accurate "all-weather" ET estimation method will be proposed through blending multi-source remote sensing data acquired by optical, thermal infrared (TIR) and passive microwave remote sensors on board polar satellite platforms. The estimation will be carried out for daily ET of the River Source Region in Southwest China, and then the remotely sensed ET results are overlapped with the footprint-weighted images of LAS (Large Aperture Scintillometer) for ground-based validation.