生产背景：黄河源区是黄河流域的主要产水区和水源涵养区，积雪融水是源区的重要水源之一，高精度的积雪面积数据集是源区生态水文模拟、气候变化等研究的基础。然而，MODIS 积雪产品中大量的云覆盖，导致了近乎一半的信息缺失。因黄河源区季节性积雪多呈现出雪层偏浅、斑块状分布且消融快等特点，使得传统统计方法很难准确捕获源区的积雪时空特征，而先进的深度学习技术能更好地深入挖掘隐藏在数据背后的时空特征。
\n生产方法：本数据集利用2000-2021年逐日的MODIS 归一化积雪指数（NDSI）产品，使用Xing等（2022）发展的基于部分卷积神经网络（PCNN）的MODIS NDSI云像元重建模型，首先生成了时空连续的MODIS NDSI数据产品；其次，再利用NASA 积雪覆盖比例（FSC）产品的标准算法，制备了黄河源区2000-2021年无缝逐日MODIS FSC遥感监测数据集。
\n数据内容：数据包含的要素是FSC，空间覆盖范围为整个黄河源区，数据起始时间为2000-2001雪季开始（即2000年11月1日），数据结束时间为2020-2021雪季结束（即2021年4月30日），其中包含21个完整积雪季。空间分辨率0.005 度（约500m），时间分辨率为逐日。命名规则为： YYYYDDD.tif，其中YYYY代表年，DDD代表儒略日（001-365）。
\n数据优势、特点及应用范围：基于黄河源区6个地面气象台站雪深观测资料和“云假设”两方面的验证表明，数据集的总体精度可以达到94%，高估和低估均为1%，平均绝对偏差10.43%，平均决定系数为0.86，表明数据具有较高精度，与晴空状态下的MODIS积雪产品具有相当的精度，本数据集可以为为黄河源区的积雪分布、雪水储量估计、积雪变化分析和雪灾风险评估等研究工作提供数据支撑。
", "ds_source": "使用的是MODIS/Terra 和MODIS/Aqua 逐日500m积雪覆盖L3级产品(MOD10A1和MYD10A1, V6)。从NASA的雪冰数据中心 (https://nsidc.org/)免费下载。
", "ds_process_way": "黄河源区MODIS FSC数据集的制作流程主要分为5个步骤：获取MODIS积雪产品、数据预处理、MODIS NDSI云像元重建（包括：MODIS/Terra和MODIS/Aqua 数据合成、临近三天时间滤波和基于PCNN的云像元时空重建）、FSC估计和精度验证
", "ds_quality": "（1）基于站点雪深观测的验证，这是最直接的验证方式。利用黄河源区上六个气象观测站点（玛多、达日、河南、久治、若尔盖、红原）2000年1月1日至2020年3月31日观测的雪深值为“真值”，对计算出的MODIS FSC进行评估。提取六个站点对应像元的MODIS FSC值，与站点实测的SD值进行比较，构建混淆矩阵，定义三个评价指标：总体精度（OA）、高估积雪事件（MO）和低估积雪事件（MU）。结果表明由于云覆盖造成的严重数据缺失，导致MODIS原始产品的总体精度不足50%。双星合成和临近时间窗口滤波，均可以将MODIS FSC产品的总体精度提升接近10%，PCNN重建所有云像元后，时空连续MODIS FSC产品的总体精度显著提高，可以达到94%，并且跟原始产品晴空像元条件下比较，并没有显著增加高估积雪事件和低估积雪事件发生的概率（高估和低估均增加1%）。\n
（2）基于云掩膜的验证，这是对基于PCNN的MODIS FSC估计模型的精确定量评价。使用31089个独立的测试块，对FSC估计结果进行精确的定量评价，定义两个定量评价指标平均绝对偏差（MAE）和决定系数（R2）。结果表明：重建的的MODIS FSC值的平均MAE为10.43%，平均R2约为0.86。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2021-04-30 00:00:00", "ds_acq_place": "黄河源区", "ds_acq_lon_east": 103.41194444444444, "ds_acq_lat_south": 32.157777777777774, "ds_acq_lon_west": 95.89, "ds_acq_lat_north": 36.55888888888889, "ds_acq_alt_low": 1961.0, "ds_acq_alt_high": 6171.0, "ds_share_type": "open-access", "ds_total_size": 34994193570, "ds_files_count": 7486, "ds_format": "tif", "ds_space_res": "500m", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "经纬度", "ds_thumbnail": "4b77f559-a668-49b0-979e-45e548947ec9.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "无", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "+869314967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.NIEER.db2115.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-05-24 16:39:01", "last_updated": "2025-04-29 14:58:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.db2115.2022", "i18n": { "en": { "title": "Data set of seamless MODIS snow coverage ratio in the source area of the Yellow River from 2000 to 2021", "ds_format": "TIF", "ds_source": "Using MODIS/Terra and MODIS/Aqua daily snow cover L3 level products (MOD10A1 and MYD10A1, V6). From NASA's Snow and Ice Data Center（ https://nsidc.org/ ）Free download
", "ds_quality": "(1) This is the most direct verification method based on site snow depth observation. Using the snow depth values observed at six meteorological observation stations in the source area of the Yellow River (Maduo, Dari, Henan, Jiuzhi, Ruoergai, and Hongyuan) from January 1, 2000 to March 31, 2020 as \"true values\", the calculated MODIS FSC was evaluated. Extract the MODIS FSC values of the corresponding pixels at six stations, compare them with the measured SD values at the stations, construct a confusion matrix, and define three evaluation indicators: overall accuracy (OA), overestimated snow cover event (MO), and underestimated snow cover event (MU). The results indicate that due to severe data loss caused by cloud coverage, the overall accuracy of MODIS original products is less than 50%. Both binary synthesis and near time window filtering can improve the overall accuracy of MODIS FSC products by nearly 10%. After reconstructing all cloud pixels using PCNN, the overall accuracy of spatiotemporal continuous MODIS FSC products is significantly improved, reaching 94%. Compared with the original product under clear sky pixel conditions, there is no significant increase in the probability of overestimating and underestimating snow events (both overestimating and underestimating increase by 1%).\n
(2) Based on cloud mask validation, this is an accurate quantitative evaluation of the MODIS FSC estimation model based on PCNN. Using 31089 independent test blocks, accurately quantitatively evaluate the FSC estimation results, and define the mean absolute deviation (MAE) and determination coefficient (R2) of two quantitative evaluation indicators. The results show that the average MAE of the reconstructed MODIS FSC value is 10.43%, and the average R2 is about 0.86
", "ds_ref_way": "", "ds_abstract": "Production background: The source area of the Yellow River is the main water producing area and water source conservation area of the Yellow River Basin. Snow melt water is one of the important water sources in the source area, and high-precision snow cover area datasets are the foundation for ecological and hydrological simulation, climate change research, and other research in the source area. However, a large amount of cloud coverage in MODIS snow products results in almost half of the information being missing. Due to the seasonal snow cover in the source area of the Yellow River showing shallow snow layers, patchy distribution, and rapid melting, traditional statistical methods are difficult to accurately capture the spatiotemporal characteristics of snow cover in the source area. Advanced deep learning techniques can better explore the spatiotemporal characteristics hidden behind the data
\nProduction method: This dataset utilizes daily MODIS Normalized Snow Index (NDSI) products from 2000 to 2021, and uses the MODIS NDSI cloud pixel reconstruction model based on Partial Convolutional Neural Network (PCNN) developed by Xing et al. (2022) to first generate spatiotemporal continuous MODIS NDSI data products; Secondly, the standard algorithm of NASA's Snow Cover Ratio (FSC) product was used to prepare a seamless daily MODIS FSC remote sensing monitoring dataset for the Yellow River source area from 2000 to 2021
\nData content: The element included in the data is FSC, with a spatial coverage of the entire Yellow River source area. The data starts from the 2000-2001 snow season (i.e. November 1, 2000) and ends on April 30, 2021, 2020. It includes 21 complete snow seasons. The spatial resolution is 0.005 degrees (approximately 500m), and the temporal resolution is daily. The naming convention is: YYYYDDD.tif, where YYYY represents the year and DDD represents the Julian day (001-365)
\nData advantages, characteristics, and application scope: Based on the verification of snow depth observation data from six surface meteorological stations in the source area of the Yellow River and the \"cloud hypothesis\", the overall accuracy of the dataset can reach 94%, with overestimation and underestimation of 1%, an average absolute deviation of 10.43%, and an average determination coefficient of 0.86. This indicates that the data has high accuracy and is comparable to MODIS snow products under clear skies, This dataset can provide data support for research on the distribution of snow cover, estimation of snow water reserves, analysis of snow cover changes, and snow disaster risk assessment in the source area of the Yellow River
", "ds_time_res": "日", "ds_acq_place": "Yellow River Source Area", "ds_space_res": "500m", "ds_projection": "Longitude and latitude", "ds_process_way": "The production process of the MODIS FSC dataset in the Yellow River source area is mainly divided into five steps: obtaining MODIS snow products, data preprocessing, MODIS NDSI cloud pixel reconstruction (including MODIS/Terra and MODIS/Aqua data synthesis, near three day time filtering, and PCNN based cloud pixel spatiotemporal reconstruction), FSC estimation, and accuracy verification
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC 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, "ds_topic_tags": [ "黄河源区", "NDSI", "FSC", "PCNN" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河源区" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "杨映", "email": "565553572@qq.com", "work_for": "甘肃洮河国家级自然保护区管护中心", "country": "中国" }, { "true_name": "唐忠喜", "email": "372179854@qq.com", "work_for": "甘肃省地质矿产勘查开发局第三地质矿产勘查院", "country": "中国" }, { "true_name": "邢德", "email": "xingde10@nudt.edu.cn", "work_for": "国防科技大学海洋气象学院", "country": "中国" }, { "true_name": "侯金亮", "email": "jlhours@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "侯金亮", "email": "jlhours@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "侯金亮", "email": "jlhours@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "积雪" }