本数据集基于地面和遥感调查数据以及积雪类型划分的成果数据，编制中国积雪类型系列图件，包括积雪密度（2017-2020年）、积雪日数（1980-2020年）、积雪深度/雪水当量（1980-2020年）和积雪反照率（2000-2020年）。所有图件均以1:100万/1:50万的数字地形图为基础地理底图，根据需要叠置中国气候区划图或土地利用/覆盖图作为背景图层。设计分级与制图符号表示方法，编制1:100万（积雪密度、积雪深度/雪水当量）/1:50万（积雪日数、积雪反照率）的中国积雪类型系列图件。数据集采用分层设色的分级方式定量表达积雪专题要素的时空分布特征，分层以积雪要素的特征和统计直方图为标准，以成图信息量最大为原则；为了更好地进行表达，设计积雪密度为紫色系、积雪日数为绿色系、积雪深度/雪水当量为蓝色系、积雪反照率为红色系。数据集以CGCS2000为坐标系统，采用正轴等角圆锥投影，参照国家测绘地理信息部门公布的标准地图（审图号：GS（2019）1815号）绘制国界
", "ds_source": "1. 积雪密度以2013-2020年全国气象观测站的逐日地面雪深和雪压观测资料、2017-2019年全国积雪观测站的地面雪深和雪压观测数据，以及2017-2019年地面调查的地面雪深和雪压观测数据作为原始数据\n 2. 积雪日数以全国范围内的1980-2020年的逐日无云AVHRR积雪覆盖长序列数据集为原始数据\n 3. 积雪深度/雪水当量以1980-2020年逐日积雪深度/雪水当量遥感反演长序列数据集为原始数据\n 4. 积雪反照率以2000-2020年的逐日积雪反照率遥感产品为原始数据
", "ds_process_way": "积雪密度=观测雪压/（观测雪深*10）。计算各个站点的积雪密度，对计算结果按照一定准则进行筛选得到有效数据，具体准则为：对雪深或雪压缺测（雪深或者雪压为32766）的单站点数据，将其剔除；对进行雪深观测但是没有进行雪压观测并将雪压记为0（雪深不为0，雪压为0）的单站点数据，将其积雪密度赋为0；对进行雪压观测但是没有进行雪深观测（雪深为0，雪压不为0）的单站点数据，将其积雪密度赋为0。对积雪密度逐日点数据进行数据筛选，剔除积雪密度大于0.5 g/cm3的点数据。
\n积雪持续日数（snow cover days，SCD）定义为整个积雪年内积雪覆盖地面的总天数。为了减轻短暂积雪的影响和积雪产品自身错误，积雪初日（snow cover onset date，SCOD）定义为积雪年内连续五天积雪的第一天，计算方法为利用Python编程，遍历一个积雪年的所有逐日像元，找到第一个连续五天为积雪，则积雪初日为第一天积雪日。如果直至次年8月31日像元一直没有连续五天积雪，则认为该像元无积雪初日，积雪初日赋值为0。积雪终日（snow cover end date，SCED）定义为积雪年内最后一个连续五天积雪的最后一天，计算方法类似于积雪初日的计算方法，遍历一个积雪年的所有逐日像元，找到最后一个连续五天积雪，则积雪终日为最后一天积雪日。如果直至8月31日像元一直没有连续五天积雪，则认为该像元无积雪终日，积雪终日赋值为0。
\n积雪反照率通过逐日数据计算逐旬和逐月平均积雪反照率，再进行多年逐月平均积雪反照率和标准偏差计算，按照标准对图例、比例尺等制图要素进行设计，并开始批量制图。
\n积雪深度/雪水当量通过逐日积雪深度/雪水当量数据，计算1980-2020年逐月平均积雪深度/雪水当量、逐月标准偏差和逐月变化率，以及积雪深度/雪水当量变化率图，按照标准对图例、比例尺等制图要素进行设计，并开始批量制图。
", "ds_quality": "首先检查制图所需数据的完整性。按照标准对图例、比例尺等制图要素进行设计，并开始批量制图。
\n然后是对地图图集进行检查，图集的质量检查主要检查图集的表达效果是否符合项目要求，地图表达规律与是否经验相符。
", "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": 135.06666666666666, "ds_acq_lat_south": 3.85, "ds_acq_lon_west": 73.51666666666667, "ds_acq_lat_north": 53.516666666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 131741217702, "ds_files_count": 2497, "ds_format": "tiff，jpg", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "0d7fe67d-25a5-48e1-ab62-b07b6956db29.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": [], "quality_level": 3, "publish_time": "2021-11-26 09:32:45", "last_updated": "2023-05-12 15:49:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2494.2022", "i18n": { "en": { "title": "Electronic map data set of temporal and spatial distribution of snow cover characteristics in China", "ds_format": "tiff，jpg", "ds_source": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt; &amp;lt;pre&amp;gt;&amp;lt;code&amp;gt;\n1. The snow density takes the daily ground snow depth and snow pressure observation data of the national meteorological observation station from 2013 to 2020, the ground snow depth and snow pressure observation data of the National Snow observation station from 2017 to 2019, and the ground snow depth and snow pressure observation data of the ground survey from 2017 to 2019 as the original data\n 2. Snow days nationwide, the daily cloudless AVHRR snow cover long series data set from 1980 to 2020 is the original data\n 3. Snow depth / snow water equivalent takes the long series data set of daily snow depth / snow water equivalent remote sensing inversion from 1980 to 2020 as the original data\n 4. Snow albedo takes the daily snow albedo remote sensing products from 2000 to 2020 as the original data
", "ds_quality": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt; &amp;lt;pre&amp;gt;&amp;lt;code&amp;gt; &amp;amp;amp;emsp;&amp;amp;amp;emsp;First, check the integrity of the data required for mapping. Design the drawing elements such as legend and scale according to the standards, and start batch drawing.\nThen check the map atlas. The quality inspection of the atlas mainly checks whether the expression effect of the atlas meets the project requirements and whether the map expression law is consistent with the experience.
", "ds_ref_way": "", "ds_abstract": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt; &amp;lt;pre&amp;gt;&amp;lt;code&amp;gt;\nBased on the ground and remote sensing survey data and the achievement data of snow type division, this data set compiles a series of maps of snow types in China, including snow density (2017-2020), snow days (1980-2020), snow depth / snow water equivalent (1980-2020) and snow albedo (2000-2020).\nAll maps are based on the digital topographic map of 1:1 million / 1:500000, and the climate zoning map or land use / cover map of China is superimposed as the background layer as required. Design classification and cartographic symbol representation methods, and prepare a series of maps of China's snow types of 1:1 million (snow density, snow depth / snow water equivalent) / 1:500000 (snow days, snow albedo).\n The data set adopts the hierarchical method of layered color setting to quantitatively express the temporal and spatial distribution characteristics of snow thematic elements. The stratification takes the characteristics of snow elements and statistical histogram as the standard and the maximum amount of mapping information as the principle; For better expression, the design snow density is purple, the number of snow days is green, the snow depth / snow water equivalent is blue, and the snow albedo is red.\n The data set takes CGCS2000 as the coordinate system, adopts positive axis isometric conic projection, and draws the national boundary with reference to the standard map published by the national surveying and mapping geographic information department (drawing review No.: GS (2019) No. 1815)
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": " <pre><code> <pre><code> &lt;pre&gt;&lt;code&gt; &amp;lt;pre&amp;gt;&amp;lt;code&amp;gt;\nSnow density = observed snow pressure / (observed snow depth * 10). Calculate the snow density of each station and screen the calculation results according to certain criteria to obtain effective data. The specific criteria are as follows: eliminate the single station data of snow depth or snow pressure (snow depth or snow pressure is 32766); For the single station data with snow depth observation but no snow pressure observation and snow pressure recorded as 0 (snow depth is not 0, snow pressure is 0), the snow density is assigned as 0; For the single station data with snow pressure observation but no snow depth observation (snow depth is 0, snow pressure is not 0), the snow density is assigned to 0. Screen the daily point data of snow density, and eliminate the point data with snow density greater than 0.5 g / cm3.\n Snow cover days (SCD) is defined as the total number of days covered by snow in the whole snow year. In order to reduce the impact of short-term snow and the error of snow products, the snow cover onset date (SCOD) is defined as the first day of five consecutive days of snow in a snow year. The calculation method is to use python programming to traverse all daily pixels of a snow year and find that the first five consecutive days is snow, then the snow cover onset date is the first day of snow. If the pixel has no snow for five consecutive days until August 31 of the next year, it is considered that the pixel has no snow on the first day, and the first day of snow is assigned as 0. Snow cover end date (sced) is defined as the last day of the last five consecutive days of snow in the snow year. The calculation method is similar to the calculation method of the first day of snow. Traverse all day-to-day pixels of a snow year and find the last five consecutive days of snow, then the snow deposition end date is the last snow day. If the pixel has no snow for five consecutive days until August 31, it is considered that the pixel has no snow all day, and the snow all day value is 0.\n Snow albedo: calculate the ten day and monthly average snow albedo based on daily data, then calculate the multi-year monthly average snow albedo and standard deviation, design the legend, scale and other mapping elements according to the standard, and start batch mapping.\n Snow depth / snow water equivalent through the daily snow depth / snow water equivalent data, calculate the monthly average snow depth / snow water equivalent, monthly standard deviation and monthly change rate from 1980 to 2020, as well as the map of snow depth / snow water equivalent change rate, design the legend, scale and other mapping elements according to the standard, and start batch mapping.
", "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_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "积雪密度", "逐月平均", "标准偏差", "积雪日数", "积雪初日", "积雪终日", "多年平均", "变化率", "积雪深度", "雪水当量", "积雪反照率" ], "ds_subject_tags": [], "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": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" }, { "true_name": "王华东", "email": "", "work_for": "南京大学", "country": "中国" }, { "true_name": "郑子贤", "email": "", "work_for": "南京大学", "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" } ], "ds_meta_authors": [ { "true_name": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张学良", "email": "zxl@nju.edu.cn", "work_for": "南京大学", "country": "中国" } ], "category": "积雪" }