本数据集利用一种全面考虑时空背景信息的时空自适应融合方法（STAR），生成了中国近 20 年的无云 Terra-Aqua MODIS NDSI 数据集。STAR NDSI 数据一般具有以下优势： (1)连续 20 年数据集，这是长期水文和气候数据集的最短周期。(2) 无云数据集可准确估算积雪动态，与原地积雪深度和大地遥感卫星 NDSI 地图高度一致。此外，STAR NDSI 采集消除了云层污染，极大地提高了 TAC NDSI 数据集的整体性能。因此，该数据集可作为水文和气候建模的基础数据集，用于探索各种关键的环境问题。", "ds_source": "
MOD10A1 和 MYD10A1 数 据 集 可 通 过 美 国 国 家 航 空 航 天 局 网 站\n（NASA:ttps://search.earthdata.nasa.gov/）获取.", "ds_process_way": "
本数据基于一种先进的 STAR 方法，该方法综合利用了时空背景信息，可以彻底去除云层。该方法分为两步：时空自适应融合（STAF）和纠错（EC）。生成新的 NDSI 地图，包括空间分区、自适应时空块确定和基于高斯核函数（GKF）的融合。考虑到积雪模式的空间异质性，首先将研究区域划分十个分区。这样，就可以在分区的基础上进行后续处理。此外，每个目标分区（T）的最佳查询分区（Q）是通过综合考虑雪变化的时间复杂性方面的时间距离（t）、区域相关性（r）和无云分数（f）确定的。
\n对于积雪变化极快且起伏不定的地区，时间背景参考很可能会引入错误信息，放大迭代过程中的误差。本数据集采用后处理方法来减少质量保证地图的 \"无序 \"现象。首先，人为确定相邻时间内积雪模式最一致的 NDSI 地图作为参考。随后应用上述 EC 技术来提高后处理区域与原始区域之间的空间一致性。最后，更新质量保证地图。", "ds_quality": "
光学遥感图像受到严重的云污染，MODIS NDSI 数据集无法准确地反映每日积雪和消融的情况。基于此，提出两阶段时空融合方法中的 STAR，以生成时空连续积雪采集，生成过程包括预处理 TAC 和关键处理 STAR。后提供一种质量评估（QA）方法，为用户提供数据可靠性文件。在此基础上，利用后处理进一步提高个别异常区域的数据质量。", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.05, "ds_acq_lat_south": 4.0, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 52331932500, "ds_files_count": 40, "ds_format": "tiff", "ds_space_res": "463m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "WGS-1984_48N", "ds_thumbnail": "9b551c50-7501-47b4-b73c-0ce1b62cf37c.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": [ "170.45" ], "quality_level": 3, "publish_time": "2023-09-22 11:27:03", "last_updated": "2026-05-20 16:16:42", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB4016.2023", "i18n": { "en": { "title": "A cloud-free MODIS NDSI dataset for China(2001–2020)", "ds_format": "tiff", "ds_source": "
The MOD10A1 and MYD10A1 datasets can be accessed through the NASA website(NASA: ttps://search.earthdata.nasa.gov/ ）Obtain", "ds_quality": "
Optical remote sensing images are severely polluted by clouds, and the MODIS NDSI dataset cannot accurately reflect daily snow accumulation and melting. Based on this, a two-stage spatiotemporal fusion method called STAR is proposed to generate spatiotemporal continuous snow collection. The generation process includes preprocessing TAC and key processing STAR. Provide a quality assessment (QA) method to provide users with data reliability files. On this basis, post-processing is used to further improve the data quality of individual abnormal areas.", "ds_ref_way": "", "ds_abstract": "
This dataset utilizes a spatiotemporal adaptive fusion method (STAR) that comprehensively considers spatiotemporal background information to generate a cloud free Terra Aqua MODIS NDSI dataset from China over the past 20 years. STAR NDSI data generally has the following advantages: (1) a continuous 20-year dataset, which is the shortest period of long-term hydrological and climatic datasets. (2) The cloud free dataset can accurately estimate snow dynamics, which is consistent with the depth of snow in situ and the height of NDSI satellite maps. In addition, STAR NDSI collection eliminates cloud pollution, greatly improving the overall performance of the TAC NDSI dataset. Therefore, this dataset can serve as a foundational dataset for hydrological and climate modeling, used to explore various key environmental issues.", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "WGS-1984_48N", "ds_process_way": "
This data is based on an advanced STAR method that comprehensively utilizes spatiotemporal background information and can completely remove cloud layers. This method consists of two steps: Spatiotemporal Adaptive Fusion (STAF) and Error Correction (EC). Generate new NDSI maps, including spatial partitioning, adaptive spatiotemporal block determination, and fusion based on Gaussian kernel function (GKF). Considering the spatial heterogeneity of snow cover patterns, the study area will first be divided into ten zones. In this way, subsequent processing can be carried out on the basis of partitioning. In addition, the optimal query partition (Q) for each target partition (T) is determined by comprehensively considering the time distance (t), regional correlation (r), and cloud free score (f) in terms of the temporal complexity of snow changes.
\r\nFor areas with rapidly changing and fluctuating snow cover, time background reference is likely to introduce erroneous information and amplify errors during the iteration process. This dataset adopts post-processing methods to reduce the \"disorder\" phenomenon in quality assurance maps. Firstly, manually determine the NDSI map with the most consistent snow cover pattern between adjacent times as a reference. Subsequently, the above-mentioned EC technology is applied to improve the spatial consistency between the post-processing area and the original area. Finally, update the quality assurance map.", "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, "belong_to_nieer": false, "ds_topic_tags": [ "中国", "modis", "NDSI" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "李星华", "email": "lixinghua5540@whu.edu.cn", "work_for": "武汉遥感与信息工程学院 大学", "country": "中国" }, { "true_name": "沈焕锋", "email": "shenhf@whu.edu.cn", "work_for": "武汉大学资源与环境科学学院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李星华", "email": "lixinghua5540@whu.edu.cn", "work_for": "武汉遥感与信息工程学院 大学", "country": "中国" }, { "true_name": "沈焕锋", "email": "shenhf@whu.edu.cn", "work_for": "武汉大学资源与环境科学学院", "country": "中国" } ], "ds_managers": [ { "true_name": "李星华", "email": "lixinghua5540@whu.edu.cn", "work_for": "武汉遥感与信息工程学院 大学", "country": "中国" }, { "true_name": "沈焕锋", "email": "shenhf@whu.edu.cn", "work_for": "武汉大学资源与环境科学学院", "country": "中国" } ], "category": "积雪" }