由于卫星轨道覆盖范围和土壤水分检索模式的限制，基于卫星的日土壤水分产品不可避免地存在全球陆地覆盖率低的缺点。SGD-SM 2.0 数据集使用了三个传感器，即 AMSR-E、AMSR2 和 WindSat。全球日降水量产品被融合到所提出的重建模型中。提出了一个集成的长短期记忆卷积神经网络（LSTM-CNN）来填补每日土壤水分产品中的空白和缺失区域。原位验证和时间序列验证证明了 SGD-SM 2.0 的重构精度和可用性（R：0.672，RMSE：0.096，MAE：0.078）。改进后的 SGD-SM 2.0 的时间序列曲线与原始的土壤水分和降水量日时间序列分布一致。与 SGD-SM 1.0 相比，改进后的 SGD-SM 2.0 在重建精度和时间序列一致性方面均有明显优势。", "ds_source": "
在本数据集中，同时融合了基于卫星的土壤水分产品和降水产品，以生成SGD-SM 2.0数据集。利用原位土壤水分位点验证了SGD-SM 2.0的重建精度。这些原位数据是从国际土壤湿度网络（ISMN）下载的。
\n从2002年到2022年，AMSR-E、AMSR2和WindSat利用了全球每日土壤水分产品。\n
\n2002-2022 年采用了 GPM （IMERG） 全球日降水量 V6 的综合多卫星 E反演产品）。这些降水产物来自多个与降水相关的卫星无源微波传感器。
\n空间分辨率表示为 0.1∘网格（约10 km）在IMERG 3级全球日ISMN汇集了全球原位地表数据，这些数据已被广泛应用于水文和土壤湿度验证。我们从2002—2022年的ISMN中选取了124个站点，并将它们与SGD-SM 2.0中相应的土壤水分产品相匹配。
", "ds_process_way": "该数据集使用 Python 3.7 语言、PyCharm 平台和 Windows 10 环境来生成无缝的全球每日土壤水分产品。
\n在硬件配置方面，我们采用 NVIDIA Titan X （Pascal） GPU、Inter E5-2609v3 CPU 和 16 GB DDR4 RAM 来执行所提出的 LSTM-CNN 模型。将全球日降水量产品与全球日土壤水分产品同时融合到SGD-SM 2.0中。建立了一种集成的长短期记忆卷积神经网络（LSTM-CNN）重构模型，以填补全球土壤每日水分产物中的空白和缺失区域。
\n最后，我们在SGD-SM 2.0数据集中递归生成无缝的每日土壤水分产品。
", "ds_quality": "从空间角度来看，所提出的SGD-SM 2.0数据集中同时表现了全球土壤水分均匀性和局部土壤水分异质性。它确保了空间的一致性，特别是对于与相邻土壤水分区域的间隙区域。除此之外，SGD-SM 2.0 中重建的区域不会反映明显的斑块或边界效应。这也证明了所提框架中部分CNN的强大能力，可以有效排除缺口或缺失土壤水分区域的无效信息。
\n从时间角度来看，所提出的SGD-SM 2.0数据集利用了互补和连续的时间序列土壤水分信息。通过融合全球日降水量，SGD-SM 2.0可以考虑单日零星的极端天气条件。此外，通过LSTM模型，可以中恢复和保存一致的时间信息。
", "ds_acq_start_time": "2002-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 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": 2551482535, "ds_files_count": 2, "ds_format": "nc", "ds_space_res": "25000", "ds_time_res": "天", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "5233d05a-0465-4109-a1bc-d46918d6be10.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": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.50", "170.55" ], "quality_level": 3, "publish_time": "2024-03-26 13:59:00", "last_updated": "2026-01-14 10:53:55", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6460.2024", "i18n": { "en": { "title": "SGD-SM 2.0: Seamless Dataset for Long term Improvement of Global Daily Soil Moisture (2002-2022)", "ds_format": "nc", "ds_source": "In this dataset, satellite based soil moisture products and precipitation products were simultaneously integrated to generate the SGD-SM 2.0 dataset. The reconstruction accuracy of SGD-SM 2.0 was validated using in-situ soil moisture sites. These in-situ data were downloaded from the International Soil Moisture Network (ISMN).
\nFrom 2002 to 2022, AMSR-E, AMSR2, and WindSat utilized global daily soil moisture products.\n
\nThe comprehensive multi satellite E inversion product of GPM (IMERG) global daily precipitation V6 was used from 2002 to 2022. These precipitation products come from multiple satellite passive microwave sensors related to precipitation.
\nThe spatial resolution is represented as 0.1∘grid (approximately 10 km), which collects global in-situ surface data at IMERG Level 3 Global Day ISMN. These data have been widely used for hydrological and soil moisture validation. We selected 124 sites from ISMN from 2002 to 2022 and matched them with corresponding soil moisture products in SGD-SM 2.0.
", "ds_quality": "From a spatial perspective, the proposed SGD-SM 2.0 dataset exhibits both global soil moisture uniformity and local soil moisture heterogeneity. It ensures spatial consistency, especially for the gap areas with adjacent soil moisture zones. In addition, the reconstructed regions in SGD-SM 2.0 do not reflect significant plaque or boundary effects. This also demonstrates the powerful ability of some CNNs in the proposed framework to effectively eliminate invalid information in areas with gaps or missing soil moisture.
\nFrom a temporal perspective, the proposed SGD-SM 2.0 dataset utilizes complementary and continuous time series soil moisture information. By integrating global daily precipitation, SGD-SM 2.0 can consider sporadic extreme weather conditions on a single day. In addition, through the LSTM model, consistent time information can be restored and saved.
", "ds_ref_way": "", "ds_abstract": "Due to the limitations of satellite orbit coverage and soil moisture retrieval modes, satellite based daily soil moisture products inevitably have the disadvantage of low global land coverage. The SGD-SM 2.0 dataset uses three sensors, namely AMSR-E, AMSR2, and WindSat. The global daily precipitation product is integrated into the proposed reconstruction model. An integrated Long Short Term Memory Convolutional Neural Network (LSTM-CNN) was proposed to fill in the gaps and missing areas in daily soil moisture products. In situ validation and time series validation have demonstrated the reconstruction accuracy and usability of SGD-SM 2.0 (R: 0.672, RMSE: 0.096, MAE: 0.078). The time series curve of the improved SGD-SM 2.0 is consistent with the original daily time series distribution of soil moisture and precipitation. Compared with SGD-SM 1.0, the improved SGD-SM 2.0 has significant advantages in reconstruction accuracy and time series consistency.
", "ds_time_res": "天", "ds_acq_place": "Global", "ds_space_res": "25000", "ds_projection": "", "ds_process_way": "This dataset uses Python 3.7 language, PyCharm platform, and Windows 10 environment to generate seamless global daily soil moisture products.
\nIn terms of hardware configuration, we use NVIDIA Titan X (Pascal) GPU, Inter E5-2609v3 CPU, and 16 GB DDR4 RAM to execute the proposed LSTM-CNN model. Integrate global daily precipitation products and global daily soil moisture products into SGD-SM 2.0 simultaneously. We have developed an integrated Long Short Term Memory Convolutional Neural Network (LSTM-CNN) reconstruction model to fill in the gaps and missing areas in daily soil moisture products worldwide.
\nFinally, we recursively generate seamless daily soil moisture products in the SGD-SM 2.0 dataset.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/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": [ "土壤湿度", "SGD-SM 2.0", "土壤水分产品", "降水产品" ], "ds_subject_tags": [ "地质学", "水文学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022 ], "ds_contributors": [ { "true_name": "袁强强", "email": "yqiang86@gmail.com", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" }, { "true_name": "金涛勇", "email": "tyjin@sgg.whu.edu.cn", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "袁强强", "email": "yqiang86@gmail.com", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" }, { "true_name": "金涛勇", "email": "tyjin@sgg.whu.edu.cn", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" } ], "ds_managers": [ { "true_name": "袁强强", "email": "yqiang86@gmail.com", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" }, { "true_name": "金涛勇", "email": "tyjin@sgg.whu.edu.cn", "work_for": "武汉大学大地测量与测绘学院", "country": "中国" } ], "category": "水文" }