Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System

Evapotranspiration (ET) represents crop water use and is a key indicator of health. Accurate estimation ET critical for agricultural irrigation resource management. retrieval using energy balance methods with remotely sensed thermal infrared data as the input has been widely applied scheduling, yield prediction, drought monitoring so on. However, limitations on spatial temporal resolution available satellite combined effects cloud contamination constrain amount detail that single can provide. Fusing from different satellites varying resolutions provide more continuous daily at field scale. In this study, we an fusion modeling system, which uses surface model to retrieve both Landsat Moderate Resolution Imaging Spectroradiometer (MODIS) then fuses MODIS timeseries Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). paper, compared STARFM implementation strategies over various lands in central California. particular, versus two Landsat-MODIS pair images explored cases rapidly changing conditions, frequently harvested alfalfa fields, well improved dual-pair method. The 30 m retrievals are evaluated flux tower observations analyzed based land cover type. This study demonstrates improvement new method standard one-pair estimating scale all major types area.