高质量的土壤水分（SM）估算对干旱监测、环境评估和农业管理等各种应用至关重要。遥感技术的进步使得利用主动和被动传感器检索近实时地球表面土壤水分成为可能。然而，欧空局气候变化倡议（CCI）SM 产品结合了来自多个传感器的数据，但由于卫星轨道限制和检索算法，牺牲了时空分辨率和覆盖范围。为解决这一问题，利用 SMAP L4 数据的高空间分辨率和 CCI 融合产品在不同土地覆被类型方面的准确性，开发了一种基于条件变异自动编码器模型的SM重建方法。通过这种方法，创建了0.0625° 的全球三天 SM 产品，时间跨度从2015年到2021 年。", "ds_source": "
欧空局 CCI SM 产品整合了来自主动和被动微波传感器的多种 SM 数据，创建了三种产品： 有源产品、无源产品和利用有源和无源微波数据的综合产品。我们收集了 CCI 的 COMBINED 微波产品，作为 SM 产品重建的基本数据源，分别将整个第 2 级观测数据重新调整为基于模式的通用气候学。该数据集跨越 40 年，空间分辨率为 0.25°，时间跨度从 1978 年 11 月至 2021 年 12 月 31 日。\n
在本研究中，我们收集了国家冰雪数据中心（NSIDC）更新的空间分辨率为9千米的SMAP第4级、全球9千米 EASE-Grid地表和根区土壤水分分析数据集。", "ds_process_way": "
我们提出了一种新的方法来重建全球范围内的HR SM产品。利用全面的欧空局CCI SM数据集和SMAP同化产品，我们采用变异推理和条件变异自动编码器（CVAE）来无缝合并这些数据集，并提高SM估计的精度和覆盖范围。这种方法克服了与缺失值、多源观测的有限使用以及空间分布差异相关的挑战。通过整合两个数据集的优势，我们获得了全球一致的高质量 SM 产品。", "ds_quality": "
重建的SM产品经过了全球核心SM站点和稀疏观测网络的严格验证。评估采用了多种指标，包括全球无偏均方根误差（ubRMSE）和相关系数（CC）。验证结果显示，核心SM站点和稀疏观测网络的 ubRMSE 值分别约为 0.029 和 0.071 m3/m3，CC 值分别约为 0.863 和 0.743。与现有基准相比，该重建产品覆盖全球，精度更高。", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -60.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 60.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 7256325527, "ds_files_count": 2, "ds_format": "NetCDF", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "22b6af04-e52c-42e6-a758-14a1215940fc.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": [ "170.45" ], "quality_level": 3, "publish_time": "2024-06-18 10:17:27", "last_updated": "2025-06-30 16:24:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6519.2024", "i18n": { "en": { "title": "Global Surface Soil Moisture Estimation Dataset Enhanced by Conditional Variation Autoencoder (2015-2021)", "ds_format": "NetCDF", "ds_source": "
The European Space Agency's CCI SM product integrates multiple SM data from active and passive microwave sensors, creating three types of products: active products, passive products, and integrated products that utilize active and passive microwave data. We collected the combined microwave products of CCI as the basic data source for SM product reconstruction, and readjusted the entire second level observation data to a pattern based general climatology. This dataset spans 40 years with a spatial resolution of 0.25 ° and a time span from November 1978 to December 31, 2021.\n
In this study, we collected the SMAP Level 4 and global 9-kilometer EASE Grid surface and root zone soil moisture analysis datasets with a spatial resolution of 9 kilometers updated by the National Snow and Ice Data Center (NSIDC).", "ds_quality": "
The reconstructed SM product has undergone rigorous validation by global core SM sites and sparse observation networks. The evaluation used multiple indicators, including global unbiased root mean square error (ubRMSE) and correlation coefficient (CC). The verification results show that the ubRMSE values of the core SM site and sparse observation network are approximately 0.029 and 0.071 m3/m3, respectively, and the CC values are approximately 0.863 and 0.743, respectively. Compared to existing benchmarks, this reconstruction product has global coverage and higher accuracy.", "ds_ref_way": "", "ds_abstract": "
High quality soil moisture (SM) estimation is crucial for various applications such as drought monitoring, environmental assessment, and agricultural management. The advancement of remote sensing technology has made it possible to use active and passive sensors to retrieve near real-time soil moisture on the Earth's surface. However, the European Space Agency's Climate Change Initiative (CCI) SM product combines data from multiple sensors, but sacrifices spatiotemporal resolution and coverage due to satellite orbit limitations and retrieval algorithms. To address this issue, a SM reconstruction method based on a conditional variation autoencoder model was developed by utilizing the high spatial resolution of SMAP L4 data and the accuracy of CCI fusion products in different land cover types. Through this method, a global three-day SM product with a temperature range of 0.0625 ° was created, spanning from 2015 to 2021.
", "ds_time_res": "", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "We propose a new approach to reconstruct HR SM products on a global scale. Using the comprehensive European Space Agency CCI SM dataset and SMAP assimilation products, we employed mutation inference and conditional mutation autoencoder (CVAE) to seamlessly merge these datasets and improve the accuracy and coverage of SM estimation. This method overcomes challenges related to missing values, limited use of multi-source observations, and spatial distribution differences. By integrating the advantages of two datasets, we have obtained globally consistent high-quality SM products.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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 （SM）", "条件变异自动编码器", "机器学习", "地表变量" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "石长江", "email": "shichangjiang20@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "张万昌", "email": "zhangwc@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "石长江", "email": "shichangjiang20@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "张万昌", "email": "zhangwc@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "石长江", "email": "shichangjiang20@mails.ucas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "张万昌", "email": "zhangwc@radi.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "category": "水文" }