Preprints
https://doi.org/10.5194/essd-2022-177
https://doi.org/10.5194/essd-2022-177
 
08 Jun 2022
08 Jun 2022
Status: this preprint is currently under review for the journal ESSD.

A 1-km daily soil moisture dataset of China based on in-situ measurement using machine learning

Qingliang Li1,2, Gaosong Shi2, Wei Shangguan1, Jianduo Li3, Lu Li1, Feini Huang1, Ye Zhang1, Chunyan Wang2, Dagang Wang4, Jianxiu Qiu4, Xingjie Lu1, and Yongjiu Dai1 Qingliang Li et al.
  • 1Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510275, China
  • 2College of Computer Science and Technology, Changchun Normal University, Changchun 130032, China
  • 3State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 10081, China
  • 4School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

Abstract. High quality gridded soil moisture products are essential for many Earth system science applications, and they are usually available from remote sensing or model simulations with coarse resolution. Here we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained with in-situ measurements of 1,789 stations, named as SMCI1.0. Random Forest is used to predict soil moisture using ERA5-land time series, leaf area index, land cover type, topography and soil properties as covariates. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2010–2020. Using in-situ soil moisture as the benchmark, two independent experiments are conducted to investigate the estimation accuracy of the SMCI1.0: year-to-year experiment (ubRMSE ranges from 0.041–0.052 and R ranges from 0.883–0.919) and station-to-station experiment (ubRMSE ranges from 0.045–0.051 and R ranges from 0.866–0.893). SMCI1.0 generally has advantages over other gridded soil moisture products, including ERA5-Land, SMAP-L4 and SoMo.ml. However, the high errors of soil moisture often located in North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to studies on the spatial-temporal patterns. As SMCI1.0 is based on in-situ data, it can be useful complements of existing model-based and satellite-based datasets for various hydrological, meteorological, and ecological analyses and modeling. SMCI1.0 can be accessed at http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

Qingliang Li et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2022-177', Joaquin Munoz-Sabater, 15 Jun 2022
    • AC1: 'Reply on CC1', Wei Shangguan, 16 Jun 2022
  • CC2: 'Comment on essd-2022-177', Joaquin Munoz-Sabater, 15 Jun 2022
    • AC2: 'Reply on CC2', Wei Shangguan, 16 Jun 2022
  • RC1: 'Comment on essd-2022-177', Anonymous Referee #1, 21 Jun 2022
    • AC3: 'Reply on RC1', Wei Shangguan, 02 Jul 2022
      • RC3: 'Reply on AC3', Anonymous Referee #1, 20 Jul 2022
    • AC4: 'Reply on RC1', Wei Shangguan, 20 Jul 2022
      • RC4: 'Reply on AC4', Anonymous Referee #1, 20 Jul 2022
  • RC2: 'Comment on essd-2022-177', Anonymous Referee #2, 02 Jul 2022
    • AC5: 'Reply on RC2', Wei Shangguan, 20 Jul 2022

Qingliang Li et al.

Data sets

A 1-km daily soil moisture dataset of China based on in-situ measurement (2010-2020) Shangguan, W., Li, Q., Shi, G. http://dx.doi.org/10.11888/Terre.tpdc.272415

Qingliang Li et al.

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Short summary
SMCI1.0 is a 1 km resolution dataset of daily soil moisture for 2010–2020 derived through machine learning trained with in-situ measurements of 1,789 stations from China. It contains 10 layers with 10 cm intervals up to 100 cm deep at daily resolution over the period 2010–2020. Compared to ERA5-Land, SMAP-L4 and SoMo.ml, SIMI1.0 has higher accuracy over China.