ChinaCropSM1 km: A fine 1km daily soil moisture dataset for dryland wheat and maize across China during 1993-2018

Abstract

Soil moisture (SM) is a key variable of the 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, currently there is rarely spatially explicit and time-continuous soil moisture information with a high resolution at the national scale. In this study, we generated a 1km soil moisture dataset for dryland wheat and maize in China (ChinaCropSM1 km) over 1993-2018 through a random forest (RF) algorithm based on numerous in situ daily observations of soil moisture. We independently used in situ observations (181327 samples) from the agricultural meteorological stations (AMSs) across China for training (164202 samples) and others for testing (17125 samples). An irrigation module was first developed according to crop type (i.e., wheat, maize), soil depth (0-10, 10-20cm) and phenology. We produced four daily datasets separately by crop type and soil depth, and their accuracies were all satisfactory (wheat r 0.93, ubRMSE 0.033m3m-3; maize r 0.93, ubRMSE 0.035m3m-3). The spatiotemporal resolutions and accuracy of ChinaCropSM1 km were 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 to other regions and crops in the world, and our improved datasets are very valuable for many studies and field management, such as agricultural drought monitoring and crop yield forecasting. The data are published in Zenodo at https://doi.org/10.5281/zenodo.6834530 (wheat0-10) (Cheng et al., 2022a), https://doi.org/10.5281/zenodo.6822591 (wheat10-20) (Cheng et al., 2022b), https://doi/org/10.5281/zenodo.6822581 (maize0-10) (Cheng et al., 2022c) and https://doi.org/10.5281/zenodo.6820166 (maize10-20) (Cheng et al., 2022d).