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2025-04-01 null null 187(卷), null(期), (null页)
Accurate and synoptic estimation of Evapotranspiration (ET) is crucial for water management. A Google Earth Engine workflow is implemented to estimate daily ET at 30m. The algorithm uses Landsat and ERA5-Land datasets and includes the Two Source Energy Balance (TSEB) model, an Artificial Neural Network for Leaf Area Index, and a gap-filling approach based on crop coefficient. The outputs were evaluated against four local flux towers in a semi-arid site in Morocco (wheat, maize, watermelon, olive), and compared to another high- resolution ET (SSEBop product). The results demonstrated good performances (RMSE between 0.67 mm/day and 2 mm/day, low MBE), while SSEBop product generally underestimated ET. Better performance of the TSEBGEE workflow was found when aggregating ET to weekly and monthly timescales. The workflow offers ease of model implementation to deliver reliable daily plot-scale ET estimates, offering the potential for broader-scale applications in semi-arid Mediterranean regions, encompassing various crops and facilitating historical analysis.