A Globally Seamless Terrestrial Evapotranspiration Dataset Retrieved by a Nonparametric Approach with Remote Sensing and Reanalysis Datasets 

Abstract. Evapotranspiration (ET) serves as a key indicator of the water change between the Earth’s surface and atmosphere, significantly influencing the hydrology cycle, surface energy cycle, and carbon cycle. Existing remote sensing models for estimating ET usually necessitate the parameterization of resistance parameters. In this study, we proposed the Remote Sensed Non-Parametric (RSNP) model, which leverages the nonparametric (NP) and Surface Flux Equilibrium-nonparametric (SFE-NP) approaches, and adapted remote sensing and reanalysis datasets of meteorological and surface parameters as model inputs. We estimate global monthly ET from 2001 to 2019 in the spatial resolution of 0.1° with RSNP model. Validation against FLUXNET sites globally yield RMSE of 23 mm/month (278 mm/yr), while regional-scale validation against water-balance ET results in a Root Mean Square Error (RMSE) of 113 mm/yr. In addition, the produced ET dataset have great accuracy in forest underlying and obtains spatial details of land surface ET. Furthermore, compared with ETMonitor, PEW and PML_V2, our dataset offers a continuous and seamless ET dataset suitable for global research. This study contributes to the advancement of global ET estimation and informs future water balance studies. The dataset presented in this article has been published in National Tibetan Plateau Data Center at https://doi.org/10.11888/Terre.tpdc.301343 (Pan, 2024).