Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product
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College of Resources and Environment, Southwest University, Chongqing 400715, China
2
Institute of RS and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
3
Beijing Key Laboratory of Spatial Information Integration & Its Applications, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yunjun Yao
Remote Sens. 2022, 14(3), 658; https://doi.org/10.3390/rs14030658
Received: 7 December 2021
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Revised: 27 January 2022
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Accepted: 27 January 2022
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Published: 29 January 2022
(This article belongs to the Special Issue Advances on Land–Ocean Heat Fluxes Using Remote Sensing)
High spatiotemporal resolution evapotranspiration (ET) data are very important for end users to manage water resources. The global ET product always has a high temporal resolution, but the spatial resolution is too low to meet the requirements of most end users. In this study, we developed a deep neural network (DNN)-based global ET product downscaling algorithm by combining remotely sensed and meteorological data sets as the input data. The relationship between global ET product and input data was built at a low spatial resolution using the DNN. Then, this relationship was applied at high spatial resolution to generate high spatial resolution ET derived from the input data with high spatial resolution. Taking the Global Land Evaporation Amsterdam Model (GLEAM) ET product as an example, downscaled ET was found to be highly consistent with the original GLEAM ET product, but to have high spatial resolution. Field validations showed that the overall coefficient of correlation and root mean square error (bias, Nash–Sutcliffe efficiency coefficient) of the downscaled GLEAM ET is 0.90 and 0.87 mm/d (−0.32 mm/d, 0.62), respectively, indicating high quality. The proposed method bridged the gaps between the global ET product and the requirements of local end users. This will benefit end users in charge of water resources management.
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Keywords:
evapotranspiration; remote sensing; downscale; deep neural network
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MDPI and ACS Style
Long, X.; Cui, Y. Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product. Remote Sens. 2022, 14, 658. https://doi.org/10.3390/rs14030658
AMA Style
Long X, Cui Y. Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product. Remote Sensing. 2022; 14(3):658. https://doi.org/10.3390/rs14030658
Chicago/Turabian StyleLong, Xunjian, and Yaokui Cui. 2022. "Spatially Downscaling a Global Evapotranspiration Product for End User Using a Deep Neural Network: A Case Study with the GLEAM Product" Remote Sensing 14, no. 3: 658. https://doi.org/10.3390/rs14030658
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