该数据集提供1978年10月24日到2012年12月31日逐日的中国范围的积雪厚度分布数据，其空间分辨率为25km。用于反演该雪深数据集的原始数据来自美国国家雪冰数据中心（NSIDC）处理的SMMR（1978-1987年)，SSM/I（1987-2008年)和AMSR-E(2002-2012)逐日被动微波亮温数据。由于三个传感器搭载在不同的平台上，所以得到的数据存在一定的系统不一致性。通过对不同传感器的亮温进行交叉定标提高亮温数据在时间上的一致性。然后利用车涛博士在Chang算法基础上针对中国地区进行修正的算法进行雪深反演。具体反演方法参考“数据说明文档”。
\n\n该数据集包含EASE-Grid和经纬度两种投影方式，分别放入两个不同的文件夹中：ease-grid_rar(数据仅到2010年)和lon-lat_rar。两种投影的数据都逐年打包，文件命名方式为：传感器名称简写+年份，如ease-grid_rar目录下的SR1985表示用SMMR亮温数据反演的1985年的雪深；SI1990表示用SSM/I亮温数据反演的1990年的雪深；AE2005表示用AMSR-E亮温数据反演的2005年的雪深，这些数据的投影方式都是EASE-Grid。lon-lat_rar目录下，上面的数据集名称解释相同，只是其投影方式为经纬度投影。详细数据说明请参考数据文档。
", "ds_source": "用于反演该雪深数据集的原始数据来自美国国家雪冰数据中心（NSIDC）处理的SMMR（1978-1987年)，SSM/I（1987-2008年)和AMSR-E(2002-2012)逐日被动微波亮温数据。
", "ds_process_way": "由于三个传感器搭载在不同的平台上，所以得到的数据存在一定的系统不一致性。通过对不同传感器的亮温进行交叉定标提高亮温数据在时间上的一致性。然后利用车涛博士在Chang算法基础上针对中国地区进行修正的算法进行雪深反演。具体反演方法参考“数据说明文档”。
", "ds_quality": "数据集通过严格的人工审核控制质量
", "ds_acq_start_time": "1978-10-24 00:00:00", "ds_acq_end_time": "2012-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 140.0, "ds_acq_lat_south": 15.0, "ds_acq_lon_west": 60.0, "ds_acq_lat_north": 55.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 402741337, "ds_files_count": 5, "ds_format": "文本数据", "ds_space_res": "25000.0m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "7e0a65e6-a4e3-4f8a-b8d9-e0f0c8d6a1a6.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "None", "ds_from_station": null, "organization_id": "9c4867b1-5cb1-4de0-abeb-df42547bf41e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967287", "ds_serv_mail": "westdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2020-04-08 11:13:29", "last_updated": "2023-08-23 16:46:28", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.Westdc.2020.641", "i18n": { "en": { "title": "A long time series dataset of snow depth of China from 1978-2012", "ds_format": "Text data", "ds_source": "The original data used to invert this snow depth dataset were derived from SMMR (1978-1987), SSM/I (1987-2008) and AMSR-E (2002-2012) daily passive microwave bright temperature data processed by the US National Snow Ice Data Center (NSIDC).
", "ds_quality": "The dataset is controlled for quality through strict manual review
", "ds_ref_way": "", "ds_abstract": "The dataset provides daily snow thickness distribution data in China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km. The original data used to retrieve the snow depth data set are from SMMR (1978-1987), SSM / I (1987-2008) and AMSR-E (2002-2012) daily passive microwave brightness data processed by the National Snow and Ice Data Center (NSIDC). Because the three sensors are installed on different platforms, there is a certain system inconsistency in the data obtained. By simultaneous interpreting the brightness temperature of different sensors, the consistency of brightness temperature data is increased. Then we use Dr. chetao's modified algorithm based on Chang algorithm to retrieve snow depth in China. Refer to \"data description document\" for specific inversion methods.
\nThe dataset contains two projections, EASE-Grid and latitude/longitude, in two different folders: ease-grid_rar (data only up to 2010) and lon-lat_rar. Data for both projections are packaged yearly, and the files are named as follows: sensor name abbreviation + year, e.g. in the ease-grid_rar directory SR1985 indicates the snow depth in 1985 as inverted with SMMR bright temperature data; SI1990 indicates the snow depth in 1990 as inverted with SSM/I bright temperature data; AE2005 indicates the snow depth in 2005 as inverted with AMSR-E bright temperature data, all of which are projected as EASE-Grid. lon-lat_rar directory, above the dataset names are interpreted the same, except that their projection is a latitude/longitude projection. Please refer to the data documentation for a detailed description of the data.
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "25000.0m", "ds_projection": "", "ds_process_way": "Since the three sensors are mounted on different platforms, there is some systematic inconsistency in the data obtained. The temporal consistency of the bright temperature data was improved by cross-calibrating the bright temperatures from the different sensors. The snow depth inversion is then carried out using Dr. Che Tao's Chang algorithm, which is modified for the area of China. For details of the inversion method, please refer to the 'Data description document'.
", "ds_ref_instruction": "In order to respect intellectual property rights, protect the rights and interests of data authors, expand the services of data centers, and evaluate the application potential of data, data users are invited to use the data\r\nStudents' research results (including published papers, treatises, data products and unpublished research reports, data products, etc.) shall be clearly indicated with data\r\nSource and data author. For data reprinted (secondary or multiple releases), the author must also indicate the source of the original data." } }, "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, "ds_topic_tags": [ "雪深" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 1978, 1979, 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 ], "ds_contributors": [ { "true_name": "车涛", "email": "chetao@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": "吴立宗", "email": "wulizong@pric.org.cn", "work_for": "国家极地科学数据中心", "country": "中国" } ], "ds_managers": [ { "true_name": "车涛", "email": "chetao@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": "遥感及产品" }