Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Gap-Filling Eddy Covariance Latent Heat Flux: Inter-Comparison of Four Machine Learning Model Predictions and Uncertainties in Forest Ecosystem

Environmental monitoring using satellite remote sensing is challenging because of data gaps in eddy-covariance (EC)-based in situ flux tower observations. In this study, we obtain the latent heat flux (LE) from an EC station and perform gap filling using two deep learning methods (two-dimensional co...

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Bibliographic Details
Main Authors: Muhammad Sarfraz Khan, Seung Bae Jeon, Myeong-Hun Jeong
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Remote Sensing
Subjects:
eddy covariance
error analysis
deep learning
machine learning
CNN
SVM
Online Access:https://www.mdpi.com/2072-4292/13/24/4976

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