Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3053-2022
https://doi.org/10.5194/essd-14-3053-2022
Data description paper
 | 
06 Jul 2022
Data description paper |  | 06 Jul 2022

Daily soil moisture mapping at 1 km resolution based on SMAP data for desertification areas in northern China

Pinzeng Rao, Yicheng Wang, Fang Wang, Yang Liu, Xiaoya Wang, and Zhu Wang

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Latest update: 28 Mar 2024
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Short summary
It is urgent to obtain accurate soil moisture (SM) with high temporal and spatial resolution for areas affected by desertification in northern China. A combination of multiple machine learning methods, including multiple linear regression, support vector regression, artificial neural networks, random forest and extreme gradient boosting, has been applied to downscale the 36 km SMAP SM products and produce higher-spatial-resolution SM data based on related surface variables.
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