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

Qiaomei Feng, Junyong Shen, Feng Yang, Shijing Liang, Jiang Liu, Xingxing Kuang, Dashan Wang, Zhenzhong Zeng

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


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 scales attests to the dataset’s satisfactory accuracy, with a pronounced emphasis on the Northern Hemisphere. Furthermore, we find that the primary driver of ET predictions varies across different climatic regions. Overall, the HG-Land ET, underpinned by the interpretability of the machine-learning model, emerges as a validated and generalized resource catering to scientific research and various applications.

Original languageEnglish
Article number908
JournalScientific data
Issue number1
Publication statusPublished - Dec 2023
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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