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

Abstract:

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agriculture irrigation application and water resource management. ET retrieval using energy balance method with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on spatial and temporal resolution of the thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). As originally developed for applications to surface reflectance, the STARFM algorithm fuses spatial information from Landsat imagery with temporal information from the coarser but more frequent MODIS imagery to produce daily estimates at Landsat-like scale (30-m). In the ET fusion system, STARFM uses the statistical information derived from Landsat ET and MODIS ET retrieved on the same date and MODIS ET on the prediction date to get Landsat-like ET estimations on all prediction dates between Landsat overpass days. In this paper, we compare different STARFM ET fusion implementation strategies over various crop lands in central California. In particular, the use of single versus two Landsat-MODIS pair images is explored in cases of rapidly changing crop conditions, as in monthly harvested alfalfa fields. The daily 30-m ET retrievals are evaluated with flux tower observation and analyzed based on land use type. Conclusions regarding optimal STARFM strategies for daily ET retrieval for various crop types will be presented and discussed.