A Comprehensive Evaluation of Five Evapotranspiration Datasets Based on Ground and GRACE Satellite Observations: Implications for Improvement of Evapotranspiration Retrieval Algorithm

نویسندگان

چکیده

Evapotranspiration (ET) is a vital part of the hydrological cycle and water–energy balance. To explore characteristics five typical remote sensing evapotranspiration datasets provide guidance for algorithm development, we used reconstructed (Recon) data based on ground GRACE satellite observations as benchmark evaluated 592 watersheds across continental United States. The Global Land Evaporation Amsterdam Model (GLEAM) dataset (with bias RMSE values 23.18 mm/year 106.10 mm/year, respectively), process-based land surface evapotranspiration/heat flux (P-LSH) (bias = 22.94 114.44 mm/year) Penman–Monteith–Leuning (PML) generated ET ?17.73 108.97 showed better performance yearly scale, followed by model tree ensemble (MTE) 99.45 141.32 moderate-resolution imaging spectroradiometer (MODIS) ?106.71 158.90 mm/year). P-LSH outperformed other four seasonal especially from March to August. Both PML MTE overall accuracy could accurately capture spatial variability in arid regions. GLEAM products were consistent with Recon middle-value section. MODIS had larger whereby tended underestimate overestimate all sections, respectively. In future, aim should be reduce algorithms further improve seasonality estimation algorithm, while improved Our analysis suggests that combining artificial intelligence or data-driven physical process will quality datasets, well enhancing their capacity applied different climate

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13122414