Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this stu...

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Bibliographic Details
Main Authors: Lior Fine, Antoine Richard, Josef Tanny, Cedric Pradalier, Rafael Rosa, Offer Rozenstein
Format: Article
Language:English
Published: MDPI AG 2022-02-01
Series:Water
Subjects:
net radiation
air temperature
wind speed
relative humidity
time series
Online Access:https://www.mdpi.com/2073-4441/14/5/763

Internet

https://www.mdpi.com/2073-4441/14/5/763

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