Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability.

ABSTRACT: Previous datasets have limitations in generalizing evapotranspiration (ET) across various land cover types due to the scarcity and spatial heterogeneity of observations, along with the incomplete understanding of underlying physical mechanisms as a deeper contributing factor. To fill in these gaps, here we developed a global Highly Generalized Land (HG-Land) ET dataset at 0.5° spatial resolution with monthly values covering the satellite era (1982-2018). Our approach leverages the power of a Deep Forest machine-learning algorithm, which ensures good generalizability and mitigates overfitting by minimizing hyper-parameterization. Model explanations are further provided to enhance model transparency and gain new insights into the ET process. Validation conducted at both the site and basin sc

SUBMITTER: Feng Q 

PROVIDER: S-EPMC10728196 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature