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

Abstract

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 1 km 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 (181 327 samples) from the agricultural meteorological stations (AMSs) across China for training (164 202 samples) and others for testing (17 125 samples). An irrigation module was first developed according to crop type (i.e., wheat, maize), soil depth (0–10, 10–20 cm) 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 0.033 m3 m−3; maize r 0.93, ubRMSE 0.035 m3 m−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).

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

“…Reference [11] also noted an improvement in correlation by 0.1 for RF generated soil moisture, compared with the default ESA-CCI product. [16] used RF to develop the 1 km resolution ChinaCropSM estimate of soil moisture that had a correlation value of 0.93 compared to a 0.35 for ESA-CCI. ubRMSE also recorded a dramatic improvement, from 0.093 to 0.033 m 3 /m 3 in this product.…”

“…ML offers advantages in handling large and noisy datasets from dynamic and non-linear systems [6][7][8][9][10]. In recent years, studies have successfully employed ML algorithms such as Random Forest (RF), gradient boost decision tree, and eXtreme Gradient Boosting (XGBoost) to enhance spatial resolution and accuracy of surface soil moisture estimates [11][12][13][14][15][16]. Notable improvements have included increased correlations and decreased unbiased root mean square error [11,[14][15][16].…”

“…In recent years, studies have successfully employed ML algorithms such as Random Forest (RF), gradient boost decision tree, and eXtreme Gradient Boosting (XGBoost) to enhance spatial resolution and accuracy of surface soil moisture estimates [11][12][13][14][15][16]. Notable improvements have included increased correlations and decreased unbiased root mean square error [11,[14][15][16]. While surface soil moisture retrievals are valuable, the scientific community increasingly recognizes the importance of deeper root-zone soil moisture (RZSM) data, as it directly influences agricultural productivity and groundwater interactions.…”

Evaluation of the Monitoring Capability of 20 Vegetation Indices and 5 Mainstream Satellite Band Settings for Drought in Spring Wheat Using a Simulation Method

“…Figure 2 illustrates the simulated canopy reflectance spectra across the five growth stages and four drought scenarios. The response of canopy reflectance to drought varied The data (Table 4) used in testing include 20 MOD09A1 images in Gansu Province from March to July 2011, 4 Sentinel-2 1C-level images of the Baoji city from March to June 2016,the soil moisture data obtained from the ChinaCropSM1 km dataset (which is a fine 1 km daily soil moisture dataset for dryland wheat and maize across China) [55], and the spatial distribution of spring wheat obtained from Luo's work on the harvest areas for three staple crops in China [56].…”