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Evapotranspiration in Korea estimated by application of a neural network to satellite images

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Title
Evapotranspiration in Korea estimated by application of a neural network to satellite images
Authors
Yeom, Jong-Min
Lee, Chang-Suk
Park, Soo-Jae
Ryu, Jae-Hyun
Kim, Jae-Jin
Kim, Hyun-cheol
Han, Kyung-Soo
Subject
Remote Sensing; Imaging Science & Photographic Technology
Keywords
Evapotranspiration; Neural network
Issue Date
2015
Citation
Yeom, Jong-Min., et al. 2015. Evapotranspiration in Korea estimated by application of a neural network to satellite images. Remote Sensing Letters, 6(6): 429-438.
Abstract
Previous biophysical and empirical models of evapotranspiration retrieval are difficult to parameterize because of the effects of the nonlinear biophysics of plants, terrestrial and solar radiation and soils, despite attempts made using various satellite products. In this study, the multilayer feed-forward neural network approach with Levenberg? Marquardt back propagation (LM-BP) was used to successfully estimate evapotranspiration using the input of various satellite-based products. When applying neural network training, value-added satellite-based products such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), air temperature and insolation are used instead of only spectral information from satellite sensors to reflect the spatial representativeness of the neural network. The evapotranspiration estimated from the neural network with input parameters showed better statistical accuracy than the MODIS products (MOD16) and Priestley?Taylor methods when compared with ground station eddy flux measurements, which were considered as reference data. Additionally, the temporal variation in neural network evapotranspiration well reflected seasonal patterns of eddy flux evapotranspiration, especially for the high cloudiness in the summer season.
DOI
10.1080/2150704X.2015.1041169
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