高亚洲是以青藏高原为主要区域的亚洲高海拔地区，是中低纬度高山积雪的重要分布区，其积雪的动态变化对水和能量平衡及区域气候具有重要的影响。由于青藏高原地区季节性积雪具有赋存时间短、雪层较薄的特点，在对水循环等问题的理解中，迫切需要日时间尺度的积雪覆盖度动态监测数据。本数据集基于空间分辨率为500m的MODIS归一化积雪指数数据，结合地形和多种云覆盖下积雪覆盖度估算算法的优势，实现云覆盖条件下的积雪覆盖度再估算，满足高亚洲地区逐日少云（＜10％）数据产品的生产要求，构建了2002～2018年高亚洲地区MODIS逐日积雪覆盖度数据集。选取无云条件下的二值积雪产品作为参考，通过云量分布和积雪总面积的时空对比，表明该产品的时空特征和二值产品具有较好的一致性。以2013年冬季为例，当积雪覆盖度大于50％时，其相关性可达0.8628。本数据集可为高亚洲地区的积雪动态监测、气候环境、水文和能量平衡、灾害评估等研究提供逐日积雪覆盖度数据。当前，数据已经被中山大学、中科院青藏高原研究所、遥感所、安徽师范大学、南京大学、河海大学、河南理工大学等学生用作硕士、博士毕业论文撰写以及用作与其他积雪产品数据精度评估等方面。
", "ds_source": "遥感反演。
", "ds_process_way": "本数据集基于空间分辨率为500 m的MODIS归一化积雪指数数据，结合地形和多种云覆盖下积雪覆盖度估算算法的优势，实现云覆盖条件下的积雪覆盖度再估算(包含MODIS上下午星数据合成、连续三天合成、短时间内“最小”冰雪覆盖、临近三像元法、八天最大陆地范围掩膜五个步骤)，满足高亚洲地区逐日少云（＜10％）数据产品的生产要求，构建了2002～2016年高亚洲地区MODIS逐日积雪覆盖度数据集。
", "ds_quality": "选取无云条件下的二值积雪产品作为参考，通过云量分布和积雪总面积的时空对比，表明该产品的时空特征和二值产品具有较好的一致性。以2013年冬季为例，当积雪覆盖度大于50％时，其相关性可达0.8628;最终的积雪覆盖度产品依然有少量的云存在，但是云量控制在10％以内.
", "ds_acq_start_time": "2002-07-01 00:00:00", "ds_acq_end_time": "2018-06-28 00:00:00", "ds_acq_place": "中国、缅甸、尼泊尔、不丹、印度等", "ds_acq_lon_east": 105.0, "ds_acq_lat_south": 26.0, "ds_acq_lon_west": 62.0, "ds_acq_lat_north": 46.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 19977442907, "ds_files_count": 18, "ds_format": "GeoTIFF", "ds_space_res": "500", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "0e277d66-d89b-4e54-8a75-fe22fcc3adee.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-01-12 09:14:43", "last_updated": "2025-04-29 14:58:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.nieer.2020.1660", "i18n": { "en": { "title": "Daily fractional snow cover dataset over High Asia during 2002 to 2018", "ds_format": "GeoTIFF", "ds_source": "Remote sensing inversion.
", "ds_quality": "Based on the comparison of cloud amount distribution and total snow area, the results show that the spatial and temporal characteristics of the product are consistent with the binary product. Taking the winter of 2013 as an example, when the snow cover is more than 50%, the correlation can reach 0.8628; the final snow cover product still has a small amount of cloud, but the cloud amount is controlled within 10%
", "ds_ref_way": "", "ds_abstract": "High altitude Asia is a high altitude region with the Qinghai Tibet Plateau as the main region. It is an important distribution area of alpine snow in middle and low latitudes. The dynamic change of snow cover has an important impact on water and energy balance and regional climate. Due to the short occurrence time and thin snow layer of the seasonal snow cover in the Qinghai Tibet Plateau, the dynamic monitoring data of snow cover in daily time scale is urgently needed in the understanding of water cycle and other issues. Based on the MODIS normalized snow cover index data with a spatial resolution of 500m, combined with the advantages of terrain and various snow cover estimation algorithms under cloud cover, this data set realizes the re estimation of snow cover under cloud cover, meets the production requirements of daily less cloud (< 10%) data products in High Asia, and constructs the MODIS daily snow cover degree in High Asia from 2002 to 2018 According to the collection. Based on the comparison of cloud amount distribution and total snow area, the results show that the spatial and temporal characteristics of the product are consistent with the binary product. Taking the winter of 2013 as an example, when the snow cover is more than 50%, the correlation can reach 0.8628. This data set can provide daily snow cover data for snow dynamic monitoring, climate and environment, hydrology and energy balance, disaster assessment and other studies in High Asia. At present, the data has been used by students of Sun Yat sen University, Institute of Qinghai Tibet Plateau, Chinese Academy of Sciences, Institute of remote sensing, Anhui Normal University, Nanjing University, Hehai University, and Henan University of technology for writing master's and doctoral dissertations, and for data accuracy evaluation with other snow products.
", "ds_time_res": "日", "ds_acq_place": "China, Myanmar, Nepal, Bhutan, India, etc", "ds_space_res": "500", "ds_projection": "", "ds_process_way": "The data set is based on a spatial resolution of 500 The MODIS normalized snow cover index data, combined with the advantages of terrain and various snow cover estimation algorithms under cloud cover, can realize the snow cover re estimation under cloud cover conditions (including MODIS satellite data synthesis in the morning and afternoon, continuous three-day synthesis, short-term \"minimum\" snow cover, near three-pixel method, eight day maximum land cover mask five steps), meeting the requirements of High Asia region According to the production requirements of daily less cloud (< 10%) data products, the MODIS daily snow cover data set of high Asian region from 2002 to 2016 was constructed.
", "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, "ds_topic_tags": [ "MODIS", "云覆盖", "积雪覆盖度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原", "高亚洲" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "邱玉宝", "email": "qiuyb@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "王星星", "email": "wangxx2017@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": "邱玉宝", "email": "qiuyb@aircas.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": "李红星", "email": "lihongxing@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" }