积雪是冰冻圈重要的组成部分，积雪覆盖范围影响地气能量平衡，进而影响气候和环境变化。积雪面积是重要的积雪参数之一，是水文和气候模型的重要输入。本数据集针对中国积雪特性，基于AVHRR-CDR反射率产品，发展了多级决策树积雪判别算法，同时利用隐马尔科夫算法、多源数据融合方法实现了产品的完全去云，制备了1980-2020年空间分辨率为5 km的逐日无云积雪面积数据集。该数据集以HDF5文件格式存储，每个HDF5文件包含18个数据要素，其中包括数据值、数据起始日期、经纬度等。同时为了快速预览积雪分布情况，逐日文件包含积雪面积缩略图，以jpg格式存储。数据集同时包含用户使用手册，方便用户使用。本数据集将根据实时卫星遥感数据和算法更新情况（目前到2020年12月份）进行持续的补充和完善，并采用完全开放共享形式。
", "ds_source": "AVHRR CDR 表面反射率来自于美国国家海洋与大气管理局（NOAA）下的气候数据记录（CDR），数据格式为hdf格式，空间分辨率为5km。
", "ds_process_way": "利用高分辨率Landsat TM数据作为真值，训练AVHRR CDR表面反射率数据，发展AVHRR CDR云雪区分算法和多级决策树积雪判别算法，通过该算法，基于GEE平台，获取初级产品，初级产品通过隐马尔科夫去云和雪深数据插值方法进行无效值和去云，基于python标准产品生产系统，最终获取研究区逐日无云积雪面积产品。
", "ds_quality": "数据质量良好。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "中国陆域", "ds_acq_lon_east": 142.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 72.0, "ds_acq_lat_north": 56.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3543153436, "ds_files_count": 29952, "ds_format": "HDF5", "ds_space_res": "5000", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "经纬度（GLL）投影", "ds_thumbnail": "b84eefb4-b6eb-45bf-9ff0-4198ffdd49d1.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "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": "2021-12-14 17:43:01", "last_updated": "2025-04-25 16:01:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.I-SNOW.2020.10", "i18n": { "en": { "title": "China Daily Cloud-free 5 km Snow Cover Extent Product Dataset from AVHRR", "ds_format": "HDF5", "ds_source": "
The AVHRR CDR surface reflectance is derived from the Climate Data Record (CDR) under the National Oceanic and Atmospheric Administration (NOAA) of the United States of America in hdf format with a spatial resolution of 5km.", "ds_quality": "
Data quality is good.", "ds_ref_way": "", "ds_abstract": "
Snowpack is an important component of the cryosphere, and the extent of snow cover affects the energy balance of the earth's atmosphere, which in turn affects climate and environmental change. Snowpack area is one of the important snowpack parameters, which is an important input to hydrological and climate models.\n
In this dataset, we developed a multi-level decision tree snow accumulation discrimination algorithm based on the AVHRR-CDR reflectance product for China's snow accumulation characteristics, and meanwhile, we utilized the Hidden Markov Algorithm and multi-source data fusion method to realize the complete de-cloudedness of the product, and prepared a day-by-day cloud-free snow area dataset with a spatial resolution of 5 km for the period of 1980-2020.\n
The dataset is stored in HDF5 file format, and each HDF5 file contains 18 data elements, including data values, data start date, latitude and longitude. Also, for a quick preview of the snow distribution, the day-by-day files contain thumbnails of the snow area, which are stored in jpg format. The dataset also contains a user manual for the convenience of users. This dataset will be continuously supplemented and improved based on real-time satellite remote sensing data and algorithm updates (currently until December 2020), and will be fully open and shared.
", "ds_time_res": "日", "ds_acq_place": "China's Land Territory", "ds_space_res": "5000", "ds_projection": "GLL", "ds_process_way": "Using high resolution Landsat TM data as the true value, training AVHRR CDR surface reflectance data, developing AVHRR CDR cloud-snow differentiation algorithm and multilevel decision tree snow accumulation discrimination algorithm, through this algorithm, based on the GEE platform, obtaining the primary product, the primary product is nulled and de-clouded by Hidden Markov de-clouded and snow depth data interpolation methods, based on the python standard product production system, and finally obtain the day-by-day cloud-free snowpack area product in the study area.", "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": [ "AVHRR", "积雪面积", "去云", "逐日", "长时间序列" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国陆域" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "纪文政", "email": "jiwenzheng@nieer.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "赵琴", "email": "zhaoqin21@mails.ucas.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "孙兴亮", "email": "0219771@stu.lzjtu.edu.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "王建", "email": "wjian@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李弘毅", "email": "lihongyi@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": "郝晓华", "email": "haoxh@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "纪文政", "email": "jiwenzheng@nieer.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": "郝晓华", "email": "haoxh@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" }