土壤水分是重要的气候变量之一，它控制着陆地与大气之间的水、碳和能量交换。准确而详细地了解土壤水分的时空分布对各种地球系统应用至关重要。作为全球陆地表面卫星（GLASS）产品套件的一部分，从 2000 年到 2020 年生成了一个长期的全球 1 公里日表面土壤湿度产品。该产品（GLASS SM）主要由 GLASS 反照率、LST 和 LAI 产品、ERA5-陆地再分析土壤水分产品以及基于集合机器学习模型的辅助数据集生成。GLASS SM 产品中包含的数据值代表最上层土壤（0-5 厘米）的体积含水量。文件以正弦投影方式存储，并以 Geo Tiff 格式提供。“节点数据 \"值设置为 -9999。
", "ds_source": "该模型是通过整合多个数据集开发的，包括来自全球陆地表面卫星 （GLASS） 产品套件的反照率、地表温度和叶面积指数产品，以及欧洲再分析 （ERA5-Land） 土壤水分产品、来自国际土壤水分网络 （ISMN） 的原位土壤水分数据集，以及辅助数据集（多误差删除改良地形 DEM 和 SoilGrids）。", "ds_process_way": "
使用集成学习模型（eXtreme Gradient Boosting—XGBoost）生成了2000-2020年全球1 km的每日时空连续土壤水分产品（GLASS SM）。", "ds_quality": "
为全面评估模型性能，探讨了三种验证策略：随机、与站点无关和与年份无关。结果表明，对于随机测试样本，采用 TC 方法选取代表性站点训练的 XGBoost 模型精度最高，总体相关系数（R）为 0.941，均方根误差（RMSE）为 0.038 m3 m-3；而对于与站点无关和与年份无关的测试样本，虽然模型的总体性能相对较低，但采用代表性站点训练模型仍可大大提高其总体精度。同时，与未进行台站筛选的模型相比，使用代表性台站训练的模型在大多数台站上的验证精度都有显著提高，各台站模型的中位 R 和无偏 RMSE（ubRMSE）分别从 0.64 提高到 0.74，从 0.055 降低到 0.052 m3 m-3 。在四个独立的土壤水分网络中对 GLASS SM 产品的进一步验证表明，它有能力捕捉测量到的土壤水分的时间动态（R = 0.69-0.89; ubRMSE = 0.033-0.048 m3 m-3）。最后，GLASS SM 产品与两个全球微波土壤水分数据集--1 千米土壤水分主动被动/圣天诺-1 L2 辐射计/雷达土壤水分产品和欧洲航天局气候变化倡议 0.25°组合土壤水分产品--之间的相互比较表明，衍生产品保持了更完整的空间覆盖，并与这两个土壤水分产品表现出高度的时空一致性。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 14183047307, "ds_files_count": 2, "ds_format": "Geo Tiff", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "正弦投影", "ds_thumbnail": "f286d29c-8552-47a9-9927-2b6ced4d1cb3.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "09314967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4599", "170.5520" ], "quality_level": 3, "publish_time": "2024-07-26 17:03:18", "last_updated": "2026-01-14 10:54:33", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6552.2024", "i18n": { "en": { "title": "Global 1km daily soil moisture products generated using ensemble learning (2000-2020)", "ds_format": "Geo Tiff", "ds_source": "This model was developed by integrating multiple datasets, including albedo, surface temperature, and leaf area index products from the Global Land Surface Satellite (GLASS) product suite, as well as soil moisture products from the European Reanalysis (ERA5 Land), in-situ soil moisture datasets from the International Soil Moisture Network (ISMN), and auxiliary datasets (Multi Error Deletion Improved Terrain DEM and SoilGrids).", "ds_quality": "
To comprehensively evaluate the performance of the model, three validation strategies were explored: random, site independent, and year independent. The results showed that for random test samples, the XGBoost model trained using the TC method to select representative sites had the highest accuracy, with an overall correlation coefficient (R) of 0.941 and a root mean square error (RMSE) of 0.038 m3 m-3; For test samples that are independent of the site and year, although the overall performance of the model is relatively low, training the model with representative sites can still greatly improve its overall accuracy. Meanwhile, compared with the model without station screening, the model trained with representative stations showed significant improvement in validation accuracy on most stations. The median R and unbiased RMSE (ubRMSE) of each station model increased from 0.64 to 0.74, and decreased from 0.055 to 0.052 m3 m-3, respectively. Further validation of the GLASS SM product in four independent soil moisture networks showed its ability to capture the temporal dynamics of measured soil moisture (R=0.69-0.89; ubRMSE=0.033-0.048 m3 m-3). Finally, the mutual comparison between the GLASS SM product and two global microwave soil moisture datasets -1 kilometer soil moisture active passive/Santiano-1 L2 radiometer/radar soil moisture product and the European Space Agency Climate Change Initiative 0.25 ° combination soil moisture product - showed that the derivative product maintained more complete spatial coverage and exhibited high spatiotemporal consistency with these two soil moisture products.
", "ds_ref_way": "", "ds_abstract": "Soil moisture is one of the important climate variables that controls the exchange of water, carbon, and energy between land and atmosphere. Accurate and detailed understanding of the spatiotemporal distribution of soil moisture is crucial for various applications in the Earth system. As part of the Global Land Surface Satellite (GLASS) product suite, a long-term global 1km daily surface soil moisture product was generated from 2000 to 2020. This product (GLASS SM) is mainly generated from GLASS albedo, LST and LAI products, ERA5 land reanalysis soil moisture products, and auxiliary datasets based on ensemble machine learning models. The data values contained in GLASS SM products represent the volumetric moisture content of the topmost soil layer (0-5 cm). The file is stored in sine projection format and provided in Geo Tiff format. The value of 'Node Data' is set to -9999
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "sinusoidal projection", "ds_process_way": "The integrated learning model (eXtreme Gradient Boosting XGBoost) was used to generate daily spatiotemporal continuous soil moisture products (GLASS SM) for 1 km worldwide from 2000 to 2020.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "土壤水分", "全球", "1公里空间分辨率" ], "ds_subject_tags": [ "地理学其他学科", "水文地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "张玉芳", "email": "zhangyuf@nwpu.edu.cn", "work_for": "武汉大学遥感与信息工程学院", "country": "中国" }, { "true_name": "梁顺林", "email": "shunlin@hku.hk", "work_for": "香港大学地理系", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张玉芳", "email": "zhangyuf@nwpu.edu.cn", "work_for": "武汉大学遥感与信息工程学院", "country": "中国" }, { "true_name": "梁顺林", "email": "shunlin@hku.hk", "work_for": "香港大学地理系", "country": "中国" } ], "ds_managers": [ { "true_name": "张玉芳", "email": "zhangyuf@nwpu.edu.cn", "work_for": "武汉大学遥感与信息工程学院", "country": "中国" }, { "true_name": "梁顺林", "email": "shunlin@hku.hk", "work_for": "香港大学地理系", "country": "中国" } ], "category": "水文" }