ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2024-495 A Globally Seamless Terrestrial Evapotranspiration Dataset Retrieved by a Nonparametric Approach with Remote Sensing and Reanalysis Datasets  Liu Suyi 1 2 3 7 Pan Xin 2 3 4 7 Yuan Jie 1 2 3 7 Tansey Kevin 5 Yang Zi 1 2 3 Wang Zhanchuan 1 2 3 Ding Xu 1 2 3 Liu Yuanbo https://orcid.org/0000-0002-8780-927X 6 Yang Yingbao 2 4 3 School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China Jiangsu Province Engineering Research Center of Water Resources and Environment Assessment using Remote Sensing, Hohai University, Nanjing 211100, China Key Laboratory of Soil and Water Processes in Watershed, Hohai University, Nanjing 210098, China College of Geography and Remote Sensing, Hohai University, Nanjing 210098, China School of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, UK Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China These authors contributed equally to this work. 17 12 2024 2024 1 26 Copyright: © 2024 Suyi Liu et al. 2024 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2024-495/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2024-495/essd-2024-495.pdf

Evapotranspiration (ET) serves as a key indicator of the water change between the Earth&rsquo;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&deg; 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 <a href="https://doi.org/10.11888/Terre.tpdc.301343" target="_blank" rel="noopener">https://doi.org/10.11888/Terre.tpdc.301343</a> (Pan, 2024).

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