Comment on essd-2022-177

Abstract

High quality gridded soil moisture products are essential for many Earth system science applications, and they are usually available from remote sensing or model simulations with coarse resolution. Here we present a 1 km resolution long-term dataset of soil moisture derived through machine learning trained with in-situ measurements of 1,789 stations, named as SMCI1.0. Random Forest is used to predict soil moisture using ERA5-land time series, leaf area index, land cover type, topography and soil properties as covariates. SMCI1.0 provides 10-layer soil moisture with 10 cm intervals up to 100 cm deep at daily resolution over the period 2010–2020. Using in-situ soil moisture as the benchmark, two independent experiments are conducted to investigate the estimation accuracy of the SMCI1.0: year-to-year experiment (ubRMSE ranges from 0.041–0.052 and R ranges from 0.883–0.919) and station-to-station experiment (ubRMSE ranges from 0.045–0.051 and R ranges from 0.866–0.893). 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 often located in North China Monsoon Region. Overall, the highly accurate estimations of both the year-to-year and station-to-station experiments ensure the applicability of SMCI1.0 to studies on the spatial-temporal patterns. As SMCI1.0 is based on in-situ data, it can be useful complements of existing model-based and satellite-based datasets for various hydrological, meteorological, and ecological analyses and modeling. SMCI1.0 can be accessed at http://dx.doi.org/10.11888/Terre.tpdc.272415 (Shangguan et al., 2022).