A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing

A Comparison of Different Machine Learning Methods to Reconstruct Daily Evapotranspiration Time Series Estimated by Thermal–Infrared Remote Sensing

Remote sensing-based models usually have difficulty in generating spatio-temporally continuous terrestrial evapotranspiration (ET) due to cloud cover and model failures. To overcome this problem, machine learning methods have been widely used to reconstruct ET. Therefore, studies comparing and evalu...

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Bibliographic Details
Main Authors: Gengle Zhao, Lisheng Song, Long Zhao, Sinuo Tao
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
evapotranspiration
two-source energy balance model
reconstruction
machine learning
validation
Online Access:https://www.mdpi.com/2072-4292/16/3/509

Internet

https://www.mdpi.com/2072-4292/16/3/509

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