Predicting future evapotranspiration based on remote sensing and deep learning

Predicting future evapotranspiration based on remote sensing and deep learning

Study region: The watersheds of the four flux sites in the United States were selected as the study areas for this research. Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. We enhanced the ConvLSTM mode...

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Bibliographic Details
Main Authors: Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Journal of Hydrology: Regional Studies
Subjects:
Actual evapotranspiration (ETa) prediction
Multi-head self-attention
Climate change
Deep learning (DL)
Online Access:http://www.sciencedirect.com/science/article/pii/S2214581824003720
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