Water, Volume 14, Issue 5
2022 March-1 - 158 articles
Cover Story: Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning gap-filling method was found to be reliable and more consistent than the standard gap-filling method, demonstrating the potential of advanced deep learning techniques for improving dynamic time series modeling. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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