本数据集基于1980-2020年5kmAVHRR逐日无云积雪面积产品，制备了中国长时间序列积雪物候数据集。数据集按照不同的物候参数共分为积雪日数、积雪初日、积雪终日3个目录，每个目录下包含40个子文件，为逐水文年积雪物候参数，目录命名规则为1980-2020年中国XXXX数据集，其中XXXX表示积雪物候参数积雪日数、积雪初日、积雪终日；子文件命名规则为NIEER_AVHRR_TTT_5000m_YYYY-YYYY.tif,其中TTT表示不同的积雪物候参数（SCD为积雪日数，SCS为积雪初日，SCM为积雪终日），YYYY-YYYY表示水文年。本数据集可服务于中国积雪时空变化分析，为气候研究，水文管理，生态环境，人文经济等科学研究、工程建设以及社会服务提供基础数据资料。
", "ds_source": "本文中使用的遥感数据为来自本中心的中国1980-2020年5 km AVHRR逐日无云积雪面积产品。该产品利用NOAA CDR AVHRR第四版本反射率数据作为输入，通过云判别算法，雪判别算法和空缺值插补算法，最终获取积雪面积产品（NIEER-GF-AVHRR-SCE）。
\n本文使用的验证数据来自中国气象局（http://data.cma.cn）地面台站观测的1980-2020年每日地面气候积雪资料数据集，当视野面积超过50%，利用米尺在北京时间8点人工测量气象站雪深。雪深精确到1 cm，雪深小于1 cm的记录为无雪，同时也记录了缺测信息。最终，有积雪记录的362个气象站被用来对积雪物候数据集进行精度评估。
", "ds_process_way": "利用逐日无云AVHRR产品作为输入值，首先对产品进行预处理，第一步，将产品的有雪栅格值（t）赋为1，无雪栅格值赋为0，获取一个只有两个值的中间数据；第二步根据公式计算每一个水文年的中国积雪日数、积雪初日和积雪终日。
", "ds_quality": "积雪日数、积雪初日和积雪终日验证相关系数R分别为0.80，0.76和0.94，RMSE分别为22.78，17.87和16.39，MAE分别为13.26，7.51和7.76，精度较高。\n
数据文件均为GeoTIFF格式，可以通过GIS与遥感软件相关的软件如ENVI、GRASS、ArcGIS等直接进行查看与应用，或者使用编程语言等相应的软件进行编译读取、计算分析等。对多年数据进行空间叠加分析，可以得到区域1980–2020年中国积雪物候区域时空分布及年纪变化趋势，可结合区域气象因素、人类活动等可以进行区域积雪变化的驱动力分析，以期可以为生产及灾害预警等提供信息服务。
", "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", "ds_acq_lon_east": 135.05, "ds_acq_lat_south": 3.85, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 34204616, "ds_files_count": 121, "ds_format": "TIF", "ds_space_res": "5000", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "392be836-6b3b-43d8-a586-7a5474a743bd.png", "ds_thumb_from": 0, "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": [ "170.45" ], "quality_level": 3, "publish_time": "2021-11-16 16:11:11", "last_updated": "2025-04-25 16:01:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2518.2022", "i18n": { "en": { "title": "AVHRR China snowpack phenology data set (1980-2020)", "ds_format": "TIF", "ds_source": "The remote sensing data used in this paper is the 5 km AVHRR day-by-day cloud-free snow area product for China from 1980 to 2020 from our center. The product utilizes NOAA CDR AVHRR version 4 reflectance data as input, and the snow accumulation area product (NIEER-GF-AVHRR-SCE) is finally obtained by cloud discrimination algorithm, snow discrimination algorithm and vacancy interpolation algorithm.
\nThe validation data used in this paper were obtained from the dataset of daily surface climatic snow information from 1980 to 2020 observed by ground stations of the China Meteorological Administration (http://data.cma.cn), where snow depths were measured manually at the weather station at 8 o'clock Beijing time when the area of the field of view was more than 50% using a meter scale. Snow depths were accurate to 1 cm, and snow depths less than 1 cm were recorded as no snow, while missing measurement information was also recorded. Ultimately, the 362 weather stations with snow records were used to assess the accuracy of the snowpack phenology dataset.
", "ds_quality": "The validation correlation coefficients R for the number of snow days, the first day of snow accumulation, and the last day of snow accumulation were 0.80, 0.76, and 0.94, respectively, the RMSE was 22.78, 17.87, and 16.39, and the MAE was 13.26, 7.51, and 7.76, respectively, which were of high accuracy.\n
The data files are in GeoTIFF format, which can be directly viewed and applied by GIS and remote sensing software such as ENVI, GRASS, ArcGIS, etc., or compiled and read, calculated and analyzed by corresponding software such as programming language.\n
The spatial superposition analysis of multi-year data can obtain the regional spatial and temporal distribution of snow cover and the age trend of snow cover in China from 1980 to 2020, which can be combined with the regional meteorological factors, human activities, etc. to analyze the driving force of the regional snow cover changes, so as to provide information services for production and disaster early warning.", "ds_ref_way": "", "ds_abstract": "
This dataset is based on the 5km AVHRR day-by-day cloud-free snow accumulation area product from 1980 to 2020, and the long time series snow accumulation and climate dataset in China is prepared. The dataset is divided into three directories according to different climatological parameters, namely, the number of snow days, the first day of snow accumulation, and the last day of snow accumulation, and each directory contains 40 subfiles for hydrological year-by-year snow accumulation and climatological parameters.\n
The naming rule of the directory is 1980-2020 China XXXX dataset, in which XXXX indicates the number of snow days, the first day of snow, and the last day of snow; and the naming rule of the subfiles is NIEER_AVHRR_TTT_5000m_YYYYY-YYYY.tif, in which TTT indicates the different snow parameters (SCD is the number of snow days, SCS is the first day of snow, SCM is the first day of snow, and SCM is the first day of snow. SCD is the number of snow days, SCS is beginning date of snow cover, and SCM is ending date of snow cover), and YYYY-YYYYY is the hydrological year.\n
This dataset can be used to analyze the spatial and temporal changes of snowpack in China, and provide basic data for climate research, hydrological management, ecological environment, humanities and economy, and other scientific researches, engineering construction and social services.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "5000", "ds_projection": "", "ds_process_way": "Using the day-by-day cloud-free AVHRR product as the input value, the product is first preprocessed. In the first step, the snow raster value (t) of the product is assigned to 1 and the no-snow raster value is assigned to 0 to obtain an intermediate data with only two values; in the second step, the number of days of snow accumulation in China, beginning date of snow cover, and ending date of snow cover for each hydrological year are calculated according to Eq.
", "ds_ref_instruction": "The data files are in GeoTIFF format, which can be viewed and applied directly through GIS and remote sensing software related software such as envi, grass, ArcGIS, or compiled, read, calculated and analyzed by using corresponding software such as programming language. Through spatial superposition analysis of multi-year data, the regional temporal and spatial distribution and age change trend of snow phenology in China from 1980 to 2020 can be obtained. The driving force of regional snow change can be analyzed in combination with regional meteorological factors and human activities, so as to provide information services for production and disaster early warning." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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", "积雪日数", "积雪初日", "积雪终日" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "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, 2020 ], "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": "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": "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": "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" } ], "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" }