Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Estimation of Daily Potential Evapotranspiration in Real-Time from GK2A/AMI Data Using Artificial Neural Network for the Korean Peninsula

Evapotranspiration (ET) is a fundamental factor in energy and hydrologic cycles. Although highly precise in-situ ET monitoring is possible, such data are not always available due to the high spatiotemporal variability in ET. This study estimates daily potential ET (PET) in real-time for the Korean P...

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Bibliographic Details
Main Authors: Jae-Cheol Jang, Eun-Ha Sohn, Ki-Hong Park, Soobong Lee
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Hydrology
Subjects:
evapotranspiration
GK2A/AMI
artificial neural network
Korean Peninsula
Online Access:https://www.mdpi.com/2306-5338/8/3/129

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