Preprints
https://doi.org/10.5194/essd-2022-254
https://doi.org/10.5194/essd-2022-254
 
01 Sep 2022
01 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018

Fei Cheng1, Zhao Zhang1,2, Huimin Zhuang1, Jichong Han1, Yuchuan Luo1, Juan Cao1, Liangliang Zhang1, Jing Zhang1, Jialu Xu1, and Fulu Tao2,3,4 Fei Cheng et al.
  • 1Academy of Disaster Reduction and Emergency Management Minsitry of Emergency Management & Ministry of Education, School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875
  • 2Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 4Natural Resources Institute Finland (Luke), FI-00790 Helsinki, Finland

Abstract. Soil moisture (SM) is a key variable of regional hydrological cycle and has important applications for water resource and agricultural drought management. Various global soil moisture products have been mostly retrieved from microwave remote sensing data. However, there is currently rare spatially explicit and time-continuous soil moisture information with a high resolution at a nation scale. Here we generated a 1km soil moisture dataset for stable crop drylands in China (ChinaCropSM1km) over 1993−2018 through random forest (RF) algorithm, based on numerous in situ daily observations of soil moisture. We used independently in situ observations (181327 samples) from the Agricultural Meteorological Stations (AMS) across China for training (164202 samples) and others for testing (17125 samples). An irrigation module was firstly developed according to crop type (i.e. wheat, maize), soil depth (0–10 cm, 10–20 cm) and phenology. We produced four daily datasets separately by crop type and soil depth, and their accuracy is all satisfactory (wheat r 0.93, ubRMSE 0.033 m3 m–3; maize r  0.93, ubRMSE 0.035 m3 m–3). The spatio-temporal resolutions and accuracy of ChinaCropSM1km are significantly better than those of global soil moisture products (e.g. r  increased by 116 %, ubRMSE decreased by 64 %), including the global remote-sensing-based surface soil moisture dataset (RSSSM) and the European Space Agency (ESA) Climate Change Initiative (CCI) SM. The approach developed in our study could be applied into other regions and crops in the world, and our improved datasets are very valuable for many studies and field managements such as agriculture drought monitoring and crop yield forecasting. The data are published in Zenodo at https://zenodo.org/record/6834530 (wheat0–10) (Cheng et al., 2022a), https://zenodo.org/record/6822591 (wheat10–20) (Cheng et al., 2022b), https://zenodo.org/record/6822581 (maize0–10) (Cheng et al., 2022c) and https://zenodo.org/record/6820166 (mazie10–20) (Cheng et al., 2022d).

Fei Cheng et al.

Status: open (until 27 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • EC1: 'Comment on essd-2022-254', Hao Shi, 04 Sep 2022 reply
    • AC1: 'Reply on EC1', zhao zhang, 05 Sep 2022 reply
  • RC1: 'Comment on essd-2022-254', Anonymous Referee #1, 09 Sep 2022 reply
  • RC2: 'Comment on essd-2022-254', Anonymous Referee #2, 12 Sep 2022 reply

Fei Cheng et al.

Data sets

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Fulu Tao, & Jialu Xu. https://doi.org/10.5281/zenodo.6834530

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Fulu Tao, & Jialu Xu. https://doi.org/10.5281/zenodo.6822591

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Fulu Tao, & Jialu Xu. https://doi.org/10.5281/zenodo.6820166

ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 Fei Cheng, Zhao Zhang, Huimin Zhuang, Jichong Han, Yuchuan Luo, Juan Cao, Liangliang Zhang, Jing Zhang, Fulu Tao, & Jialu Xu. https://doi.org/10.5281/zenodo.6822581

Fei Cheng et al.

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Short summary
We generated a 1 km daily soil moisture dataset for crop drylands across China (ChinaCropSM1km) over 1993−2018 through random forest regression, based on in situ observations. Our improved products have a remarkably better quality compared with the public global products in terms of both spatial and time dimension by integrating an irrigation module (crop type, phenology, soil depth). The dataset may be useful for agriculture drought monitoring and crop yield forecasting studies.