Improving satellite-based actual evapotranspiration estimations using data from local weather stations

Evapotranspiration (ET) is crucial for water resource management, agricultural planning, and understanding land-atmosphere interactions. Numerous approaches are available for estimating ET at various spatial and temporal scales, including ground-based measurements, mechanistic models, and remote sensing. In this study, we aimed to enhance the accuracy and applicability of the Sentinel for Evapotranspiration (Sen-ET) plugin for estimating ET in diverse field crops in Israel. The primary objectives were to validate the Sen-ET method using eddy covariance (EC) measurements across various seasons and crop types, improve Sen-ET estimates by incorporating local weather station data, and illustrate the influence of weather station distance from measurement sites on Sen-ET accuracy.

The research was conducted across eight test sites in Israel, including fields with spring wheat, potato, cotton, and tomato. In applying Sen-ET model, we utilized high-resolution Sentinel-2 and Sentinel-3 imagery, along with ERA-5 meteorological data and local weather station inputs. The ET estimations by Sen-ET involved preprocessing satellite data, resampling meteorological data, and using a Two Source Energy Balance model to derive daily ET values. These estimates were compared against EC measurements.

The results demonstrated that incorporating local weather station data significantly improved the accuracy of the Sen-ET estimates, with most sites showing a substantial reduction in root mean square error (RMSE) of daily ET compared to the standard Sen-ET method. For example, at one of the wheat sites, the RMSE was reduced from 0.60 mm to 0.14 mm day^-1. On the other hand, one of the tomato sites showed a slight deterioration, with an increase of 0.01 mm day^-1 in RMSE when data from a weather station 7 km away was used. However, when a closer weather station at 1.17 km was used, the RMSE was reduced by 0.34 mm day^-1, thus demonstrating the importance of employing representative weather data in the model.

This study underscores the contribution of localized meteorological data in refining satellite-based ET models and provides a robust approach for precise ET estimation in agricultural landscapes. The findings have significant implications for improving water resource management and irrigation practices.