ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018
- 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)
Fei Cheng et al.
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 https://doi.org/10.5281/zenodo.6834530
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 https://doi.org/10.5281/zenodo.6822591
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 https://doi.org/10.5281/zenodo.6820166
ChinaCropSM1km: a fine 1km daily Soil Moisture dataset for Crop drylands across China during 1993–2018 https://doi.org/10.5281/zenodo.6822581
Fei Cheng et al.
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