A 1 km daily soil moisture dataset over China using in situ measurement and machine learning

[摘要] High-quality gridded soil moisture products are essential for many Earth system science applications, while the recent reanalysis andremote sensing soil moisture data are often available at coarse resolutionand remote sensing data are only for the surface soil. Here, we present a 1 km resolution long-term dataset of soil moisture derived through machinelearning trained by the in situ measurements of 1789 stations over China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). Random forest is used as a robust machine learning approach to predict soil moisture using ERA5-Land time series, leaf area index, landcover type, topography and soil properties as predictors. SMCI1.0 provides10-layer soil moisture with 10 cm intervals up to 100 cm deep at dailyresolution over the period 2000–2020. Using in situ soil moisture as the benchmark, two independent experiments were conducted to evaluate the estimationaccuracy of SMCI1.0: year-to-year (ubRMSE ranges from 0.041 to 0.052 and R ranges from 0.883 to 0.919) and station-to-station experiments (ubRMSE ranges from 0.045 to 0.051 and R ranges from 0.866 to 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 are often located in the North China Monsoon Region. Overall, the highly accurate estimations of both theyear-to-year and station-to-station experiments ensure the applicability ofSMCI1.0 to study the spatial–temporal patterns. As SMCI1.0 is based on in situ data, it can be a useful complement to existing model-based and satellite-based soil moisture datasets for various hydrological,meteorological, and ecological analyses and models. The DOI link for the dataset is http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).

[学科分类] 眼科学