针对中国积雪分布区，基于混合像元雪水当量反演算法，利用星载被动微波遥感亮温数据制备了1980-2020年空间分辨率为25km的逐日雪水当量/雪深数据集。该数据集以HDF5文件格式存储，每个HDF5文件包含5个数据要素，其中包括雪深（cm）、雪水当量（mm）、经纬度、质量标识符等。同时为了快速预览积雪分布情况，逐日文件包含雪水当量缩略图，以png格式存储。本数据集将根据实时卫星遥感数据和算法更新情况（目前到2020年1月份）进行持续的补充和完善，并采用完全开放共享。
\nH5文件中的像素值有特定含义： \"0-240 \"表示 SWE 的有效值，SWE 单位为毫米；\"250 \"表示干雪，\"251 \"表示湿雪，\"252 \"表示自由雪，\"253 \"表示水体，\"254 \"表示数据缺失，\"255 \"表示中国境外。为了定位像素位置，H5 文件中还包括经纬度矩阵。
", "ds_source": "本数据集通过混合像元雪水当量反演算法，基于IDL标准产品生产系统，利用SMMR（1980-1987）、SSMI（1988-2008）、SSMIS（2009-2020）卫星遥感亮温数据生产而来。
", "ds_process_way": "基于星载被动微波遥感亮温数据（SMMR/SSMI），利用混合像元雪水当量反演算法制备了空间分辨率为25km的逐日雪水当量/雪深数据集。生产过程中考虑了混合像元的影响(森林、草地、农田)；利用波段组合去除了地形的影响；引入大气模型降低了大气带来的反演不确定性；拟合森林参数降低了森林带来的反演不确定性；并针对产品的一致性进行了偏差校正。
\n为了尽最大可能保证空间范围的全覆盖，针对亮温数据进行升降轨合并，如果仍然存在轨道间隙，则采用前后两天数据平均的方式填补。如果只有前天或后天存在数据，则采用前天或后天的数据填补。对于SMMR，由于时间分辨率为2天，因此只采用升降轨合并。卫星观测亮温数据集来自美国冰雪数据中心（https://nsidc.org/data/）， 数据格式为bin格式，空间分辨率为25km，采用EASE-GRID投影方式。
", "ds_quality": "将 SWE 估算值与 2011-2019 年期间气象站的测量值进行了比较。总体无偏均方根误差（unRMSE）和偏差值分别为 5.09 厘米和-0.65 厘米。相关系数（corr.coe）为 0.84（在 0.05 置信区间内，p < 0.01），表明地面测量值与雪深估计值之间存在显著关系。
", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-01-31 00:00:00", "ds_acq_place": "中国陆域", "ds_acq_lon_east": 142.0, "ds_acq_lat_south": 16.0, "ds_acq_lon_west": 72.0, "ds_acq_lat_north": 56.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 85699541150, "ds_files_count": 26732, "ds_format": "HDF5", "ds_space_res": "25000", "ds_time_res": "日", "ds_coordinate": "无", "ds_projection": "EASE-GRID(N1)", "ds_thumbnail": "63c5cebb-587d-42cf-bd81-6f1325f1e165.png", "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": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-07-14 20:28:35", "last_updated": "2023-10-27 09:55:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.I-SNOW.2020.6", "i18n": { "en": { "title": "Daily snow water equivalent product from 1980 to 2020 over China", "ds_format": "HDF5", "ds_source": "1_Weather station measurements and snow course data
\nIn situ snow depth measurements were from the weather stations and field survey course in China. The daily weather station measurements during the period 1980–2020 were provided by the National Meteorological Information Centre, China Meteorology Administration. The measured snow parameters are snow depth (daily) and snow pressure (every five-day), namely SWE. The dataset of daily station measurements can be accessed by scientific researchers through the submission of an application (http://data.cma.cn/en). The field snow campaign supported by the Chinese snow survey project was conducted from 2017 to 2019 winter months, and provides an important validation dataset for this study This dataset can be available from the corresponding author on request (Wang et al., Citation2018).
\n2_Satellite observations
\nOwing to the similar configurations and inter-sensor calibrations between the SSM/I and SSMIS, thus these instruments are selected to provide brightness temperature data from 1987 to 2020 (https://nsidc.org/data/NSIDC-0032/versions/2). The brightness temperature data during the period 1980–1987 were acquired from the SMMR on board the Nimbus-7 Pathfinder satellite (https://nsidc.org/data/NSIDC-0071/versions/1). The SMMR, SSM/I and SSMIS Equal-Area Scalable Earth-Grid (EASE-Grid) brightness temperature product at 25 km × 25 km resolution were used in this study.
\n3_Auxiliary data
\nIn this paper, a linear unmixing algorithm was used to generate a 40-year snow depth dataset from 1980 to 2020. To develop the linear unmixing algorithm, the land cover fraction data are necessary.
", "ds_quality": "The SWE estimates were compared to the weather station measurements during the period 2011–2019 . The overall unbiased root mean square error (unRMSE) and bias values are 5.09 cm and −0.65 cm, respectively. The correlation coefficient (corr.coe) is 0.84 (p < 0.01 at 0.05 confidence interval), representing the significant relationship between ground-based measurements and snow depth estimates.", "ds_ref_way": "", "ds_abstract": "
This work presents a daily SWE product of 1980–2020 with a linear unmixing method through passive microwave data including SMMR, SSM/I and SSMIS over China after cross-calibration and bias-correction.
\nThe pixel values in the H5 files have specific meanings: “0–240” represents the effective value of SWE, and the units of SWE is mm; “250” is for dry snow, “251” is for wet snow, “252” is for the free snow, “253” is for the water body, “254” means missing data and “255” is for outside China. To position the pixel location, the latitude and longitude matrices were also included in the H5 file.
", "ds_time_res": "日", "ds_acq_place": "Chinese mainland", "ds_space_res": "25000", "ds_projection": "EASE-GRID(N1)", "ds_process_way": "In this paper, we applied a semi-empirical model with the linear unmixing method (hereafter, called LUM algorithm) designed for China’s snow cover (Jiang et al., Citation2014; Yang et al., Citation2018) to estimate snow depth. Then, a parameterized snow density (180 kg/m3) (Yang et al., Citation2019a) was used to transfer snow depth to SWE according to the ground-based measurements. A satellite passive microwave pixel usually covers several land use types due to its coarse spatial resolution.
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "雪水当量", "雪深", "被动微波", "亮温", "遥感" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国陆域" ], "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": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "杨建卫", "email": "yangjianwei@mail.bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "戴礼云", "email": "dailiyun@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": "李晓峰", "email": "lixiaofeng@iga.ac.cn", "work_for": "中国科学院东北地理与农业生态研究所", "country": "中国" }, { "true_name": "邱玉宝", "email": "qiuyb@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" }, { "true_name": "武胜利", "email": "", "work_for": "中国气象局国家卫星气象中心 ", "country": "中国" }, { "true_name": "李震", "email": "lizhen@aircas.ac.cn", "work_for": "中国科学院空天信息创新研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "蒋玲梅", "email": "jiang@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "积雪" }