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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Cited articles

Abbaszadeh, P., Moradkhani, H., and Zhan, X.: Downscaling SMAP Radiometer Soil Moisture Over the CONUS Using an Ensemble Learning Method, Water Resour. Res., 55, 324–344, https://doi.org/10.1029/2018WR023354, 2019. 
Achieng, K. O.: Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models, Comput. Geosci., 133, 104320, https://doi.org/10.1016/j.cageo.2019.104320, 2019. 
Ågren, A. M., Larson, J., Paul, S. S., Laudon, H., and Lidberg, W.: Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape, Geoderma, 404, 115280, https://doi.org/10.1016/j.geoderma.2021.115280, 2021. 
Akoglu, H.: User's guide to correlation coefficients, Turkish J. Emerg. Med., 18, 91–93, https://doi.org/10.1016/j.tjem.2018.08.001, 2018. 
Bai, J., Cui, Q., Zhang, W., and Meng, L.: An Approach for Downscaling SMAP Soil Moisture by Combining Sentinel-1 SAR and MODIS Data, Remote Sensing, 11, 2736, https://doi.org/10.3390/rs11232736, 2019. 
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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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