本数据集以中国2000-2019年积雪面积500m逐日无云产品为基础资料，制备了2000-2020年MODIS中国积雪物候数据集，包括三个目录，目录命名规则为2000-2020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_MODIS_TTT_500m_YYYY-YYYY.tif,其中TTT表示不同的积雪物候参数，YYYY-YYYY表示水文年，例如NIEER_MODIS_SCD_5000m_2001-2002.tif，空间分辨率为500m，时间分辨率为1年。\n
使用本中心提供的中国2000-2019年积雪面积500m逐日无云产品，按照积雪物候各参数积雪日数（Snowcoverdays,SCD）、积雪初日（Startofsnowcover,SCS）、积雪终日（Meltofsnowcoverdays，SCM）相应定义，制备了2000-2020年MODIS中国积雪物候数据集，按各参数不同分为三个目录，并用地面台站数据进行精度验证。本数据集以期能够为积雪的深入研究与准确分析、动物保护、气候预测、农业水资源利用、洪水、雪灾预警等领域提供基础数据。
", "ds_source": "研究使用的遥感数据为来自本中心的中国2000-2019年积雪面积500m逐日无云产品(NIEER-GF-MODIS-SCE)。该产品根据高空间分辨率的无云Landsat-5 TM/Landsat8 OLI影像，在中国的林区和非林区分别改进MODIS产品标准的积雪提取算法，并使用隐马尔可夫时空建模和微波雪深数据插值两部去云，并结合温度数据、水体数据制备的2000-2020年中国长时间序列逐日无云积雪面积产品（分辨率为5 km），包含5个波段。
\n验证数据雪深数据为来自中国气象数据网（http://data.cma.cn）的2000-2020年地面气候积雪资料日值数据集。记录数据主要包括气象台站区号、经纬度、海拔、年月日以及雪深、平均气温、雪压、平均风速以及最大风速风向等信息，雪深无效值为32700或32766。
", "ds_process_way": "首先对积雪面积产品进行预处理，将产品的有雪栅格值（t=1,2,3）赋为1，无雪栅格（t=0,4,255）值赋为0，一个水文年定义为9月1日到次年8月31日，然后按照积雪物候参数的定义逐水文年逐像元计算中国2000-2020年积雪日数、积雪初日、积雪终日。
", "ds_quality": "积雪日数验证相关系数R2为0.94、RMSE为12.09天，MAE为7.60天；积雪初日的R2为0.79、RMSE为12.24天，MAE为4.6天；积雪终日的R2为0.56、RMSE为19.89天，MAE为7.74天，精度较高。\n
该数据文件均为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2000–2020年中国积雪物候区域时空分布及变化趋势，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": "2000-01-01 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": 1238945153, "ds_files_count": 61, "ds_format": "TIF", "ds_space_res": "500", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "15157e5e-6c73-4d7e-91de-d769fef0a509.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": "09314967592", "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 16:02:49", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2519.2022", "i18n": { "en": { "title": "MODIS snow cover and phenology dataset for China, 2000-2020", "ds_format": "TIF", "ds_source": "
The remote sensing data used in the study is a day-by-day cloud-free product (NIEER-GF-MODIS-SCE) of 500m snow accumulation area in China from 2000-2019 from our center. This product is based on the high spatial resolution cloud-free Landsat-5 TM/Landsat8 OLI imagery, and improves the standard snow accumulation extraction algorithm of the MODIS product in forested and non-forested areas in China, respectively, and uses two-part de-clouding by Hidden Markov Spatio-Temporal Modeling and interpolation of microwave snow depth data, and combines with the temperature data, and the water data to prepare the long time series of China for the period of 2000-2020 Day-by-day cloud-free snowpack area products (with a resolution of 5 km) containing five bands.\n
The validation data snow depth data are the daily value dataset of surface climate snow accumulation information from 2000-2020 from China Meteorological Data Network (http://data.cma.cn). The recorded data mainly include meteorological station area number, latitude, longitude, elevation, month and day of the year, as well as information on snow depth, average air temperature, snow pressure, average wind speed, and maximum wind speed and direction, with an invalid value of 32700 or 32766 for snow depth.
", "ds_quality": "The validation correlation coefficients for the number of snow days were R2 of 0.94, RMSE of 12.09 days, and MAE of 7.60 days; for the first day of snow accumulation, R2 of 0.79, RMSE of 12.24 days, and MAE of 4.6 days; and for the last day of snow accumulation, R2 of 0.56, RMSE of 19.89 days, and MAE of 7.74 days, with a high precision.\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 using programming languages and other corresponding software. The spatial overlay analysis of multi-year data can be used to obtain the regional spatial and temporal distribution and change trend of China's snowpack from 2000 to 2020, which can be combined with the regional meteorological factors and human activities to analyze the driving force of the regional snowpack changes, so as to provide information services for production and disaster early warning.", "ds_ref_way": "", "ds_abstract": "
This dataset is prepared based on the day-by-day cloud-free product of 500m snow area in China from 2000 to 2019, and the 2000-2020 MODIS China snow phenology dataset is prepared, including three directories, with the directory naming rule of 2000-2020 China XXXX dataset, in which XXXX denotes the number of days of snow accumulation, the first day of accumulation, and the last day of accumulation of the snow phenology parameter; and sub The file naming rule is NIEER_MODIS_TTT_500m_YYYYY-YYYY.tif, in which TTT represents different snow cover parameters, and YYYYY-YYYY represents hydrological year, such as NIEER_MODIS_SCD_5000m_2001-2002.tif, with a spatial resolution of 500m and a temporal resolution of 1 year. year.\n
Using the day-by-day cloud-free product of 500m snow cover area in China from 2000-2019 provided by the Center, according to the corresponding definitions of each parameter of snow cover climate, snow cover days (Snowcoverdays,SCD), snow cover first days (Startofsnowcover,SCS), snow cover last days (Meltofsnowcoverdays,SCM). The MODIS snow cover data set for China from 2000 to 2020 was prepared.\n
The dataset was divided into three catalogs according to the different parameters, and the accuracy was verified by ground station data. This dataset is intended to provide basic data for the in-depth study and accurate analysis of snow cover, animal protection, climate prediction, agricultural water resource utilization, flooding, snowstorm warning and other fields.
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "500", "ds_projection": "", "ds_process_way": "Firstly, the snow area product was preprocessed by assigning the snow raster value (t=1,2,3) of the product to 1, and the no-snow raster (t=0,4,255) value to 0. A hydrological year was defined as September 1 to August 31 of the following year, and then the number of snow days, the first day of snow, and the last day of snow were calculated for China from 2000 to 2020 according to the definitions of the snow climate parameter on a water year by water year basis and image by image basis.", "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. The spatial superposition analysis of multi-year data can obtain the regional temporal and spatial distribution and change trend of China's snow phenology from 2000 to 2020. 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": "CC BY 4.0", "ds_topic_tags": [ "MODIS", "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": [ 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": "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": "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": "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": "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": "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": "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" } ], "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" }