积雪面积比例（fractionalsnowcover，FSC）是定量描述单位像元内积雪覆盖面积（SnowCoverArea，SCA）与像元空间范围的比值。本数据集利用GoogleEarthEngine（GEE）平台，将北极地区（北纬45°至北纬90°）的FSC和MOD09GA1000m的全球地表反射率产品作为元数据用于制备北极积雪面积比例时序数据。数据集时间序列为2000年2月24日至2019年11月18日。该数据集可为区域气候模拟、水文模型等提供积雪分布的定量信息。
", "ds_source": "源数据为MOD09GA 1000 m的全球地表反射率产品，数据下载网址为：https://ladsweb.modaps.eosdis.nasa.gov/", "ds_process_way": "
本数据集制备方法为：基于MODIS09GA数据建立FSC制备的线性回归经验模型，用Landsat 8地表反射率数据和SNOMAP算法制备的FSC数据作为参考数据集，选取其中一部分参考数据作为模型的训练数据，另一部分作为模型的检验数据。\n
每日观测数据通过求平均值或合计值获得当日数据。空气温度、空气湿度、气压、风速、不同层次土壤温度取日平均值，降水量、水面蒸发量取日合计值，冻土深度取每日8时观测值，空气最高最低温度、地表最高最低温度、最高最低气压取前一日20时至当日20时的最高最低值，日照时数取每日20时观测值。整理后的数据获得逐日数据存储。", "ds_quality": "
根据NDSI与FSC之间建立的线性回归模型制备FSC的精度要比考虑植被对FSC的影响建立的二元线性回归模型的精度要低。在两个线性模型的验证结果中，基于MODIS制备FSC的方法的精度最低。", "ds_acq_start_time": "2000-02-24 00:00:00", "ds_acq_end_time": "2019-11-18 00:00:00", "ds_acq_place": "泛北极", "ds_acq_lon_east": -180.0, "ds_acq_lat_south": 35.0, "ds_acq_lon_west": 180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": 8848.0, "ds_share_type": "apply-access", "ds_total_size": 818041236179, "ds_files_count": 14094, "ds_format": ".tif", "ds_space_res": "1000m", "ds_time_res": "日", "ds_coordinate": "其他", "ds_projection": "Lambert投影", "ds_thumbnail": "ebf67503-296a-4c8d-8791-a85586f75d38.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据由“中国科学院西北生态环境资源研究院”产生，用户在使用数据时请在正文中明确注明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "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": "10.12072/ncdc.I-SNOW.db0023.2021", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-12-06 15:46:57", "last_updated": "2023-03-06 12:14:25", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2493.2022", "i18n": { "en": { "title": "Time series of Fractional Snow Cover Product in the Arctic Spanning 2000-2019", "ds_format": "", "ds_source": "
<pre><code>\nThe source data is the global surface reflectance product of mod09ga 1000 m, and the data download website is: https://ladsweb.modaps.eosdis.nasa.gov/", "ds_quality": "
<pre><code> <p>&emsp; The accuracy of the linear regression model between NDSI and FSC is lower than that of the binary linear regression model considering the influence of vegetation on FSC. Among the verification results of the two linear models, the method of preparing FSC based on MODIS has the lowest accuracy.\n <pre><code> <p>&emsp; The fractional snow cover (FSC) is a quantitative description of the ratio of snow cover area (SCA) in a unit pixel to the spatial range of pixels. Using Google Earth engine (GEE) platform, this data set uses FSC and mod09ga 1000 m global surface reflectance products in the Arctic (45 ° to 90 ° north latitude) as metadata to prepare time series data of Arctic snow area proportion. The time series of the data set is from February 24, 2000 to November 18, 2019. The data set can provide quantitative information of snow distribution for regional climate simulation and hydrological model.\n <pre><code>\nThe preparation method of this data set is: establish the linear regression empirical model prepared by FSC based on modis09ga data, use Landsat 8 surface reflectance data and FSC data prepared by snap algorithm as the reference data set, select one part of the reference data as the training data of the model and the other part as the test data of the model.\n
The daily observation data is obtained by calculating the average value or total value. The daily average value of air temperature, air humidity, air pressure, wind speed and soil temperature at different levels is taken, the daily total value of precipitation and water surface evaporation is taken, the daily observation value of 8:00 is taken for the depth of frozen soil, the highest and lowest air temperature, the highest and lowest surface temperature and the highest and lowest air pressure are taken from 20:00 of the previous day to 20:00 of the current day, and the daily observation value of 20:00 is taken for the sunshine hours. The sorted data is obtained and stored day by day.", "ds_ref_instruction": " This data is generated by Northwest Institute of ecological environment and resources, Chinese Academy of Sciences. When using the data, users should clearly indicate the source of the data in the text, and quote the reference method provided by this metadata in the References section." } }, "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": [ "北极地区", "积雪面积比例（FSC）", "Google Earth Engine" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泛北极" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "马媛", "email": "may15@lzu.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": "赵宏宇", "email": "zhaohongyu@lzb.ac.cn", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "邵东航", "email": "shaodonghang@lab.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": "王卫国", "email": "1473201406@qq.com", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李浩杰", "email": "563339015@qq.com", "work_for": "西北师范大学", "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": "马媛", "email": "may15@lzu.edu.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": "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" } ], "category": "积雪" }