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Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability
Qiaomei Feng1, Junyong Shen2, Feng Yang1
1School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Scientific Data
|December 18, 2023
Summary
A new Highly Generalized Land (HG-Land) evapotranspiration (ET) dataset offers improved global accuracy for the satellite era. This machine learning-driven dataset enhances understanding of ET processes across diverse land cover types.
Area of Science:
- Earth Science
- Environmental Science
- Remote Sensing
Background:
- Existing evapotranspiration (ET) datasets struggle with generalization across diverse land cover types.
- Data scarcity, spatial heterogeneity, and incomplete physical mechanism understanding limit current ET datasets.
Purpose of the Study:
- To develop a globally applicable, highly generalized evapotranspiration (ET) dataset.
- To improve the understanding of ET processes and drivers across different climatic regions.
Main Methods:
- Developed the Highly Generalized Land (HG-Land) ET dataset (1982-2018) at 0.5° spatial resolution.
- Utilized a Deep Forest machine-learning algorithm for enhanced generalizability and reduced overfitting.
- Incorporated model explanations for transparency and new insights into ET.
Main Results:
- The HG-Land ET dataset demonstrates satisfactory accuracy validated at site and basin scales, particularly in the Northern Hemisphere.
- Identified varying primary drivers of ET predictions across different climatic zones.
- The dataset provides monthly ET values covering the satellite era.
Conclusions:
- The HG-Land ET dataset is a validated, generalized resource for scientific research and applications.
- The interpretability of the machine-learning model enhances transparency and understanding of ET.
- This dataset addresses limitations of previous ET datasets, offering broader applicability.
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