Li Lingcheng, Lin Xinming, Fang Yilin, Hou Z Jason, Leung L Ruby, Wang Yaoping, Mao Jiafu, Xu Yaping, Massoud Elias, Shi Mingjie
Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, United States.
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN, 37830, United States.
Sci Data. 2025 Apr 1;12(1):546. doi: 10.1038/s41597-025-04657-x.
A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM's spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land-atmosphere interactions and making recommendations for drought response planning.
在美国大陆（CONUS）开发了一个统一的集合土壤湿度（SM）数据包。该数据包包括来自陆面模型、遥感、再分析和机器学习模型的19种产品。所有数据集都统一到0.25度的月度时空分辨率，提供了地表SM动态的全面视图。数据集的统计分析利用柯本-盖革气候分类法来探索地表SM的时空变异性。提取的SM特征突出了不同的模式，美国大陆西部显示出较大的变异系数值，而美国大陆东部则表现出较高的SM值。遥感数据集往往较干燥，而再分析产品呈现出较湿润的条件。原位SM观测作为小波功率谱分析的基础，以解释各数据集在时间尺度上的差异，促进每日SM记录。本研究提供了一个全面的土壤湿度数据包和一个分析框架，可用于地球系统模型评估和不确定性量化、量化干旱影响和陆气相互作用，并为干旱应对规划提供建议。
