本数据集提供逐日 SMOS-IB 亮温（TB）、土壤水分（SM）与植被光学深度（VOD）产品。该数据集基于 SMOS 卫星观测数据，依托 L-MEB 辐射传输模型重构得到降噪后的 40° 单角度亮温序列。优化后的 40° 亮温与 SMAP 卫星观测几何保持一致，大幅削弱了法国土壤湿度数据中心（CATDS）多角度 SMOS 三级亮温产品中存在的高频噪声。\n
研究采用 SMAP-INRAE-BORDEAUX（SMAP-IB）反演算法，并结合更新后的地表粗糙度数据集，生成 SMOS-IB 土壤水分与植被光学深度产品（Li 等，2022；Konkathi 等，2025）。将该产品与国际土壤水分网络（ISMN）实测土壤水分、多类植被代用指标开展对比验证，结果表明其精度优于多角度 SMOS 标准产品。数据集时间跨度为 2010-2024 年，空间分辨率 25 km，提供全球逐日 40° 入射角亮温、土壤水分及植被光学深度数据，适用于 L 波段反演算法开发、SMAP 数据一致性同化、全球干旱监测，以及植被水分与生物量动态相关研究。\n
开展各类应用分析或精度验证前，需对数据进行严格质量控制。数据筛选仅需借助场景标识层（Scene_Flags，简写 SF）与均方根误差层（RMSE）两类栅格。\n
筛选步骤如下：\n
1.采用条件 SF ≤ 1 过滤逐日土壤水分 / 植被光学深度数据，剔除地形干扰严重、地表冻结及无线电污染区域的无效像元；\n
2.设定亮温均方根误差阈值 TB-RMSE ≤ 8 K 或 TB-RMSE ≤ 6 K，去除强射频干扰（RFI）像元。该阈值可按需调整：阈值过大会降低土壤水分、植被光学深度数据质量；阈值过小则会过滤掉大量有效逐日观测样本。阈值选择需结合研究目标，全球尺度验证研究通常选用 6 K 或 8 K。\n
所有数据采用 netCDF4 格式存储，变量为 64 位双精度浮点型；空间网格采用全球第二代等面积可扩展地球网格（EASE-Grid 2.0），空间分辨率 25 千米。单个文件栅格尺寸为 584 行 ×1388 列。\n
注意事项\n
1.植被光学深度（VOD）正常取值范围为 [0, 2]，土壤水分（SM）正常取值范围为 [0, 1]；\n
2.部分区域年中值 VOD/SM 出现负值，该类数值无物理意义，已统一赋值为 0（异常像元主要分布于撒哈拉沙漠与澳大利亚中部）。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 13884686464, "ds_files_count": 0, "ds_format": "NetCDF4", "ds_space_res": "25km", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "891af79c-7c85-4854-bbf8-aa64e6b657d8.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "使用 SMOS-IB 数据集必须引用以下文献：\r\n1.Xing et al., 2025. An operational global L-band soil moisture and vegetation optical depth dataset from optimized 40° SMOS brightness temperatures. ESSD. (In submission)\r\n2.Li, X., Wigneron, J. P., Frappart, F., De Lannoy, G., Fan, L., Zhao, T., ... & Ciais, P. (2022). The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations. Remote Sensing of Environment, 282, 113272.\r\n3.Li, X., Ciais, P. et al., Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022. (2025). Nature Communications. https://doi.org/10.1038/s41467-025-59999-2", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.2055", "170.45" ], "quality_level": 0, "publish_time": "2026-06-18 09:52:03", "last_updated": "2026-06-18 10:16:17", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.remote_sensing.db7456.2026", "i18n": { "en": { "title": "An operational global L-band soil moisture and vegetation optical depth dataset from optimized 40° SMOS brightness temperatures", "ds_format": "NetCDF4", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
This repository provides daily SMOS-IB brightness temperature (TB), soil moisture (SM), and vegetation optical depth (VOD) products, derived from SMOS observations after reconstructing a noise-reduced 40° mono-angular TB record using the L-MEB radiative transfer model. The optimized 40° TB is consistent with the SMAP viewing geometry and substantially reduces the high-frequency noise present in the CATDS multi-angular SMOS Level-3 TB.\r\n
SMOS-IB SM and VOD were then retrieved using the SMAP-INRAE-BORDEAUX (SMAP-IB) algorithm with updated soil roughness map (Li et al., 2022; Konkathi et al., 2025). Evaluations against ISMN soil moisture measurements and multiple vegetation proxies show improved performance compared with multi-angular SMOS products. The dataset provides global daily 40° TB, SM, and VOD at 25 km from 2010 to 2024, suitable for L-band algorithm development and SMAP harmonization, global drought monitoring, and studies of vegetation water and biomass dynamics.\r\n
Before doing any application or validation studies, the quality control of the data should be done carefully. For the data filtering, we just need to use Scene_Flags (SF) and RMSE layers.\r\n
Firstly, we usually filter the daily SM/VOD values by the conditions “SF <= 1” to remove the strong Topo, frozen scene and polluted scene. Then, we use TB-RMSE <= 8k or TB-RMSE <= 6k to remove strong RFI impact (this RMSE threshold can be higher or lower, but a higher value will reduce the quality of the SM/VOD, and a lower value will mask out too many daily observations; this value depends on your application, usually for global scale validation, we choose 6 or 8K).", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "Citation of these papers is compulsory when you use the SMOS-IB product:\r\n1.Xing et al., 2025. An operational global L-band soil moisture and vegetation optical depth dataset from optimized 40° SMOS brightness temperatures. ESSD. (In submission)\r\n2.Li, X., Wigneron, J. P., Frappart, F., De Lannoy, G., Fan, L., Zhao, T., ... & Ciais, P. (2022). The first global soil moisture and vegetation optical depth product retrieved from fused SMOS and SMAP L-band observations. Remote Sensing of Environment, 282, 113272.\r\n3.Li, X., Ciais, P. et al., Large live biomass carbon losses from droughts in the northern temperate ecosystems during 2016-2022. (2025). Nature Communications. https://doi.org/10.1038/s41467-025-59999-2" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "亮温", "土壤水分", "植被光学深度" ], "ds_subject_tags": [ "放射性地球物理学", "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "李小军", "email": "xiaojunli_vod@163.com", "work_for": "西南交通大学地球科学与工程学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李小军", "email": "xiaojunli_vod@163.com", "work_for": "西南交通大学地球科学与工程学院", "country": "中国" } ], "ds_managers": [ { "true_name": "李小军", "email": "xiaojunli_vod@163.com", "work_for": "西南交通大学地球科学与工程学院", "country": "中国" } ], "category": "遥感及产品" }