Preprints
https://doi.org/10.5194/essd-2021-362
https://doi.org/10.5194/essd-2021-362
 
07 Dec 2021
07 Dec 2021
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

Pinzeng Rao1,2, Yicheng Wang2, Fang Wang2, Yang Liu2, Xiaoya Wang3, and Zhu Wang2 Pinzeng Rao et al.
  • 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
  • 2State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
  • 3State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Abstract. Land surface soil moisture (SM) plays a critical role in hydrological processes and terrestrial ecosystems in areas affected by desertification. Passive microwave remote sensing products such as the Soil Moisture Active Passive (SMAP) have been shown to monitor surface soil water well. However, the coarse spatial resolution and lack of full coverage of these products greatly limit their application in areas undergoing desertification. In order to overcome these limitations, a combination of multiple machine learning methods, including multiple linear regression (MLR), support vector regression (SVR), artificial neural networks (ANN), random forest (RF) and extreme gradient boosting (XGB), have been applied to downscale the 36 km SMAP SM products and produce higher spatial-resolution SM data based on related surface variables, such as vegetation index and surface temperature. Areas affected by desertification in Northern China, which are very sensitive to SM, were selected as the study area, and the downscaled SM with a resolution of 1 km on a daily scale from 2015 to 2020 was produced. The results show a good performance compared with in situ observed SM data, with an average unbiased root mean square error value of 0.049 m3/m3. In addition, their time series are also consistent with precipitation and perform better than some common gridded SM products. The data can be used to assess soil drought and provide a reference for reversing desertification in the study area. This dataset is freely available at https://doi.org/10.6084/M9.FIGSHARE.16430478.V5 (Rao et al., 2021).

Pinzeng Rao et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-362', Anonymous Referee #1, 30 Dec 2021
  • AC1: 'Comment on essd-2021-362', pinzeng rao, 30 Dec 2021
  • CC1: 'Comment on essd-2021-362', Jie Dong, 05 Feb 2022
    • CC2: 'Reply on CC1', pinzeng rao, 13 Feb 2022
  • RC2: 'Comment on essd-2021-362', Anonymous Referee #2, 04 Mar 2022
  • EC1: 'Comment on essd-2021-362', Martin Schultz, 10 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-362', Anonymous Referee #1, 30 Dec 2021
  • AC1: 'Comment on essd-2021-362', pinzeng rao, 30 Dec 2021
  • CC1: 'Comment on essd-2021-362', Jie Dong, 05 Feb 2022
    • CC2: 'Reply on CC1', pinzeng rao, 13 Feb 2022
  • RC2: 'Comment on essd-2021-362', Anonymous Referee #2, 04 Mar 2022
  • EC1: 'Comment on essd-2021-362', Martin Schultz, 10 Mar 2022

Pinzeng Rao et al.

Data sets

Daily soil moisture mapping at 1 km resolution based on SMAP data for areas affected by desertification in Northern China Pinzeng RaoPinzeng Rao, Yicheng Wang, Fang Wang, Yang Liu, Xiaoya Wang, Zhu Wang https://doi.org/10.6084/M9.FIGSHARE.16430478.V5

Pinzeng Rao et al.

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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, have been applied to downscale the 36 km SMAP SM products and produce higher spatial-resolution SM data based on related surface variables.