基于站点观测的中国1km土壤湿度日尺度数据集（2000-2022）

本研究提供了中国范围1km高质量的土壤湿度数据集-SMCI1.0(Soil Moisture of China by in situ data, version 1.0)，SMCI1.0是包含2000-2022年、日尺度、以10厘米为间隔10层深度（10-100cm）的高时空分辨率土壤湿度，数据单位为0.001m³/m³，缺失值为-999，投影为WGS1984。该数据集是以中国气象局提供的1,648个站点观测10层土壤湿度作为基准，使用ERA5_Land气象强迫数据、叶面积指数（LAI）、土地覆盖类型（Landtypes）、地形（DEM）和土壤特性（Soil properties）作为协变量，通过机器学习方式获得。本研究进行了两组实验以验证SMCI1.0的精度，时间尺度上：ubRMSE为0.041-0.052，R为0.883-0.919；空间尺度上：ubRMSE为0.045-0.051，R为0.866-0.893。 由于SMCI1.0是基于实地观测的土壤湿度，它可以作为现有基于模型和卫星数据集的有效补充。该数据产品可用于各种水文、气象、生态分析和建模，尤其在需要高质量、高分辨率土壤湿度的应用上至关重要。有关数据集的引用及详细描述，请阅读说明文档。为便于使用，本研究提供了两种不同分辨率的版本：30 秒（~1km）和0.1度（~9km）。

This study presents a high-quality 1 km spatial resolution soil moisture dataset covering China, named SMCI1.0 (Soil Moisture of China by in situ data, version 1.0). SMCI1.0 is a high spatiotemporal resolution soil moisture product spanning 2000 to 2022 at the daily scale, with 10 layers at 10 cm intervals (10–100 cm depth). The dataset uses a unit of 0.001 m³/m³, and missing values are denoted as -999. The coordinate projection is WGS 1984. This dataset was developed using 10-layer soil moisture observations from 1,648 in-situ stations provided by the China Meteorological Administration as the ground truth, with ERA5_Land meteorological forcing data, Leaf Area Index (LAI), land cover types, Digital Elevation Model (DEM) and soil properties as covariates, via machine learning methods. Two groups of validation experiments were conducted to assess the accuracy of SMCI1.0: at the temporal scale, the unbiased root mean square error (ubRMSE) ranges from 0.041 to 0.052, and the Pearson correlation coefficient (R) ranges from 0.883 to 0.919; at the spatial scale, the ubRMSE ranges from 0.045 to 0.051, and R ranges from 0.866 to 0.893. As SMCI1.0 is based on in-situ measured soil moisture, it can serve as an effective complement to existing model-based and satellite-derived soil moisture datasets. This data product can be applied to various hydrological, meteorological and ecological analyses and modeling, and is particularly critical for applications requiring high-quality, high-resolution soil moisture data. For dataset citations and detailed descriptions, please refer to the accompanying documentation. To facilitate usage, two versions with different spatial resolutions are provided in this study: 30 arc-seconds (~1 km) and 0.1 degree (~9 km).

搜集汇总

背景与挑战

背景概述

该数据集是基于中国气象局1,648个站点观测数据，通过机器学习方法生成的2000-2022年中国范围日尺度土壤湿度数据，空间分辨率达1km，覆盖10层深度（10-100cm），精度较高（如时间尺度上R值为0.883-0.919）。它可作为现有模型和卫星数据的有效补充，适用于水文、气象和生态分析等需要高分辨率土壤湿度的应用。