本数据集采用基于三维离散余弦变换的惩罚最小二乘回归方法，首先生成无缝全球每日L-VOD产品，然后采用非局部滤波思想实现高分辨率与低分辨率数据的时空融合，最终生成2010年1月1日至2021年7月31日期间的全球每日无缝9公里L-VOD产品。为验证产品质量，对重建数据进行了时间序列验证和模拟缺失区域验证。融合产品在时空上均经过验证，并在重叠期间与原始9公里数据进行了数值比较。结果显示，无缝SMOS（SMAP）数据集在模拟真实缺失掩膜下的决定系数（R²）为0.855（0.947），均方根误差（RMSE）为0.094（0.073）。重建的每日L-VOD产品的时空一致性与原始有效值的时间序列分布相符。融合产品与原始9公里数据在重叠时段的空间信息基本一致（R²：0.926–0.958，RMSE：0.072–0.093，平均绝对误差MAE：0.047–0.064）。融合产品与原始产品的时间变化基本同步。本数据集可在自然灾害（如洪水、干旱和森林火灾）期间提供及时的植被信息，支持早期灾害预警和实时响应。
", "ds_source": "1、L-VOD 数据
（1）SMOS IC L-VOD 数据集由欧洲航天局 （ESA） 发布，卫星重访周期为 8 d，空间分辨率为 25 km，空间覆盖范围为全球，使用 2010 年 1 月 1 日至 2017 年 12 月 31 日期间的最新改进版 2 的 L-VOD 数据，来自 https://ib.remote-sensing.inrae.fr/index.php/smos-ic-v2-product-documentation/， 这些数据有助于构建基线数据并为目标时刻生成 9 公里的 L-VOD 数据。
（2）SMAP MT-DCA L-VOD 数据集覆盖全球表面，卫星重访周期为 3 d，空间分辨率为 9 km。该数据集使用了 Feldman 和 Entekhabi （2019） 发布的最新 SMAP MT-DCA 第 5 版 L-VOD 2015 年 4 月 1 日至 2021 年 7 月 31 日的数据，来自 https://doi.org/10.5281/zenodo.5619583 ，将其用作时空融合模型中的高分辨率基线数据，为 VOD 融合产品提供精细的空间细节信息。
2、辅助数据
（1）基于 MODIS MCD12C1 V061，时间跨度为 2001 年至 2022 年，空间分辨率为 0.05°，每年一次的全球土地覆盖类型数据。
（2）基于 MODIS MYD13C1 V061 ，其空间分辨率为 0.05°，16 d 合成，2010 年至 2021 年的 NDVI 数据。
", "ds_process_way": "1、对于所选的 VOD_smos 和 VOD_smap 数据集，需要执行预处理步骤，例如重新投影、异常处理和重新采样；
2、填补空缺值；
3、数据融合；
4、算法设置。
", "ds_quality": "由于缺乏原位 L-VOD 数据，采用了三种验证策略来评估数据：（1） 时间序列验证，（2） 模拟缺失区域验证，以及 （3） 数据比较验证。通过定量和定性评估，我们发现融合产物VOD_st有效地保持了VOD_resmos稳定的长期特征，并实现了良好的空间一致性。它在数值上非常接近 VOD_resmap，从而缓解了与 SMOS 卫星衍生的 L-VOD 产品相关的低估问题。
", "ds_acq_start_time": "2010-01-01 00:00:00", "ds_acq_end_time": "2021-07-31 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": 21797792038, "ds_files_count": 14, "ds_format": "*.mat", "ds_space_res": "9000", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d2cd0fde-9ba5-416c-803f-595683ba0ae6.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": [], "quality_level": 3, "publish_time": "2025-06-27 17:03:55", "last_updated": "2026-01-14 10:44:16", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6892.2025", "i18n": { "en": { "title": "Global daily seamless 9-kilometer vegetation optical depth (VOD) product (2010-2021)", "ds_format": "*.mat", "ds_source": "1. L-VOD data
(1) The SMOS IC L-VOD dataset is released by the European Space Agency (ESA), with a satellite revisit period of 8 days, a spatial resolution of 25 km, and a global spatial coverage. This study used the latest improved version 2 L-VOD data from January 1, 2010 to December 31, 2017, sourced from https://ib.remote-sensing.inrae.fr/index.php/smos-ic-v2-product-documentation/ These data help to construct baseline data and generate 9-kilometer L-VOD data for the target time.
(2) The SMAP MT-DCA L-VOD dataset covers the global surface, with a satellite revisit period of 3 days and a spatial resolution of 9 km. The dataset uses the latest SMAP MT-DCA 5th edition L-VOD data published by Feldman and Entekhabi (2019) from April 1, 2015 to July 31, 2021, sourced from https://doi.org/10.5281/zenodo.5619583 Using it as high-resolution baseline data in spatiotemporal fusion models to provide fine spatial detail information for VOD fusion products.
2. Auxiliary data
(1) Based on MODIS MCD12C1 V061, the time span is from 2001 to 2022, with a spatial resolution of 0.05 °, and annual global land cover type data.
(2) Based on MODIS MYD13C1 V061, with a spatial resolution of 0.05 °, 16 day synthesis, NDVI data from 2010 to 2021.
", "ds_quality": "Due to the lack of in-situ L-VOD data, three validation strategies were employed to evaluate the data: (1) time series validation, (2) simulation of missing regions validation, and (3) data comparison validation. Through quantitative and qualitative evaluations, we found that the fusion product VOD_st effectively maintains the stable long-term characteristics of VOD_resmos and achieves good spatial consistency. It is very close in numerical value to VOD_resmap, thus alleviating the underestimation problem related to L-VOD products derived from SMOS satellites.
", "ds_ref_way": "", "ds_abstract": "This dataset adopts a penalty least squares regression method based on three-dimensional discrete cosine transform. Firstly, seamless global daily L-VOD products are generated. Then, non local filtering ideas are used to achieve spatiotemporal fusion of high-resolution and low resolution data. Finally, a global daily seamless 9-kilometer L-VOD product is generated from January 1, 2010 to July 31, 2021. To verify product quality, time series validation and simulated missing area validation were performed on the reconstructed data. The fusion product has been validated in both time and space, and compared numerically with the original 9-kilometer data during the overlap period. The results showed that the coefficient of determination (R ²) of the seamless SMOS (SMAP) dataset under simulated real missing masks was 0.855 (0.947), and the root mean square error (RMSE) was 0.094 (0.073). The spatiotemporal consistency of the reconstructed daily L-VOD product is consistent with the time series distribution of the original effective values. The spatial information of the fusion product and the original 9-kilometer data during the overlapping period is basically consistent (R ²: 0.926-0.958, RMSE: 0.072-0.093, average absolute error MAE: 0.047-0.064). The time changes of the integrated product and the original product are basically synchronized. This dataset can provide timely vegetation information during natural disasters such as floods, droughts, and forest fires, supporting early disaster warning and real-time response.
", "ds_time_res": "日", "ds_acq_place": "Global", "ds_space_res": "9000", "ds_projection": "", "ds_process_way": "1. For the selected VOD_smos and VOD_smap datasets, preprocessing steps such as reprojection, exception handling, and resampling need to be performed;
2. Fill in vacancies;
3. Data fusion;
4. Algorithm settings.
", "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": [ "VOD", "全球", "植被光学深度", "时空融合" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "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" } ], "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" } ], "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" } ], "category": "其他" }