本数据集利用多源遥感数据，生成1980年以来北极冰川面积、厚度、冰储量、物质平衡、运动速度和表面温度数据。共选取16条研究区内的典型冰川作为研究对象，分布如图1所示。数据集制作方法如下：基于Landsat数据，采用目视解译方法生成北极地区1985、1995、2005、2015、2024年共五期冰川编目，并提取对应时段的冰川面积；基于两期已公开的冰川厚度与储量数据，通过均值处理，获取研究时段内目标冰川的厚度与储量信息；物质平衡数据来源于已发表相关文献以及模型重建；基于ITS_LIVE冰川运动速度数据，提取目标冰川1985-2022年的运动速度；基于ERA5再分析数据确定目标冰川1985-2024年逐月温度，按照冰川编目划定冰川表面温度获取范围，以范围内温度均值代表冰川表面温度。", "ds_source": "
（1）冰川面积数据，基于Landsat系列卫星数据获取，数据于美国地质勘探局（https://earthexplorer.usgs.gov/） 及地理空间数据云（http://www.gscloud.cn/） 下载。该系列卫星搭载多光谱扫描仪（MSS）、专题制图仪（TM）、增强型专题制图仪（ETM+）、操作陆地成像仪（OLI）等传感器，分辨率覆盖15-100米，时间分辨率为16天。\n
（2）冰川厚度和储量数据，冰川厚度数据源于Millan et al. (2022)全球冰川表面流速和冰厚数据集。\n
（3）冰川物质平衡数据源自实地观测、文献收集以及现有数据集。本数据集中Novaya Zemlya Archipelago、PARRISH和WYKEHAM GLACIER SOUTH三条冰川数据源自文献，文献如下①Ciracì E, Velicogna I, Sutterley T C. Mass balance of Novaya Zemlya archipelago, Russian High Arctic, using time-variable gravity from GRACE and altimetry data from ICESat and CryoSat-2[J]. Remote Sensing, 2018, 10(11): 1817.\n②Millan R, Mouginot J, Rignot E. Mass budget of the glaciers and ice caps of the Queen Elizabeth Islands, Canada, from 1991 to 2015[J]. Environmental Research Letters, 2017, 12(2): 024016. McCall、West Gulkana、Eklutna、Wolverine 、Alexander、Lemon Creek、Taku、Yuri和Andrei、MITTIVAKKAT、GEITLANDSJOKULL、AUSTRE BROEGGERBREEN和ENGABREEN十三条冰川数据源自现有数据集。具体来源如下；\n现有数据集源自世界冰川监测服务（world glacier monitoring service）的冰川波动数据库Fluctuations of Glaciers (FoG) Database。该数据库是基于原位测量、遥感和重建的国际收集的关于冰川状态和变化（长度、面积、体积、质量）的标准化数据集。\n
（4）冰川运动速度数据源自ITS_LIVE（The inter-mission Time Series of Land Ice Velocity and Elevation）数据集，提取自Landsat4、5、7、8卫星影像，涵盖了所有面积大于5 km2的陆地冰区，时间跨度为1985—2022年。该数据有120 m和240 m两种分辨率，本数据集使用由光学卫星图像对生成的120 m分辨率的速度数据。该数据获取网站（https：//its-live.jpl.nasa.gov/）。\n
（5）冰川温度数据是来自欧洲中期天气预报中心（ECMWF）的第五代大气再分析产品（ERA5），是一种综合性的再分析数据，本数据集中利用大气再分析产品获取气温数据。数据获取网站（https：//cds.climate.copernicus.eu/#！/home）", "ds_process_way": "
（1）冰川面积。基于光学遥感影像的比值阈值法和雪盖指数法是当前冰川边界提取最为常用的方法。由于本研究中部分冰川区被表碛覆盖且高质量影像不足，为了准确量化冰川边界，我们以 RGI V7 冰川编目为参考，通过将多种特征参数组合获得边界范围，然后将结果在 ArcGIS 软件中进行人工修订，最后基于人工修订的结果研究该区域冰川面积变化。人工修订时应尽量去除积雪和阴影的影响，确保冰川边界尽量光滑，避免锯齿状边界，矢量线尽量贴近实际冰川边界，避免线段拐点中存在巨大跳跃。\n
（2）冰川厚度及储量信息，冰川冰厚度的估算基于表面运动和基底滑动的浅冰近似（shallow-ice approximation，SIA）模型。表面速度和基底速度的关系通过公式计算，其中考虑冰的密度、重力加速度以及冰表面和基底之间的高度差。基底速度通过引入比例因子与表面速度的关系来估算。冰厚度计算考虑了速度、冰表面坡度和密度的影响，最终为所有冰川提供了一个连通度小于2的冰川网络的冰厚度估算值。基于获取的冰川厚度资料，通过厚度-面积的乘积获取对应的冰川储量。
（3）冰川物质平衡为文献收集数据，未涉及加工方法
（4）冰川运动速度获取。基于IT_LIVE数据与冰川中流线数据提取，利用ArcGIS软件中栅格裁剪工具，将每条冰川的中流线速度提取，并计算其均值。
（5）冰川温度获取。基于ERA5数据与冰川边界数据提取，利用ArcGIS软件中栅格裁剪工具，将每条冰川的温度进行提取，并计算其均值。", "ds_quality": "
（1）冰川面积不确定性，本数据集仅考虑Landsat遥感影像空间分辨率引起的误差，计算公式如下：ε= N*A。其中ε为冰川面积误差（平方公里）；N为冰川边界周长；A 为半个像元的长度（15 m）。
（2）模型验证通过使用现场数据来检验模型与实测数据之间的差异是否源于过度拟合。为此，研究团队在阿尔卑斯地区移除了60%的厚度数据进行建模，并与移除的数据进行对比。结果显示，模型与实测数据的差异为−16±51米，表明模型未发生过度拟合。同时不同冰厚范围误差分析显示，大于100米的冰层误差为25%-35%，小于100米的误差超过50%。
（3）物质平衡数据质量控制均与数据源相同；
（4）冰川运动速度，相关数据质量及误差计算可参考ITS_LIVE数据说明文档（http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/documentation/ITS_LIVE-Regional-Glacier-and-Ice-Sheet-Surface-Velocities.pdf） \n
（5）冰川温度，相关数据质量及误差计算可参考（https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation）", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "北极区域", "ds_acq_lon_east": 135.0, "ds_acq_lat_south": 45.0, "ds_acq_lon_west": 135.0, "ds_acq_lat_north": 45.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 24659077, "ds_files_count": 54, "ds_format": "*.tif，*.shp，*.xlsx", "ds_space_res": "50m", "ds_time_res": "月、年", "ds_coordinate": "WGS84", "ds_projection": "Transverse_Mercator Projection System ", "ds_thumbnail": "6e38b964-484b-4ada-abfe-02bc3341ff4e.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "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": "2026-05-07 09:51:16", "last_updated": "2026-05-07 09:51:16", "protected": false, "protected_to": "2028-03-13 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7131.2026", "i18n": { "en": { "title": "Arctic Glacier Dataset", "ds_format": "", "ds_source": "
(1) Glacier area data is derived from Landsat satellite series data, obtained from the https://earthexplorer.usgs.gov/ (USGS) and the Geospatial Data Cloud (http://www.gscloud.cn/). This satellite series carries sensors including the Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI), with spatial resolutions ranging from 15 to 100 meters and a temporal resolution of 16 days.\r\n
(2)Glacier thickness and volume data: Glacier thickness data are sourced from Millan et al. (2022) global glacier surface velocity and ice thickness dataset.\r\n
(3) Glacier mass balance data are sourced from field observations, literature collection, and existing datasets. In this dataset, data for the Novaya Zemlya Archipelago, PARRISH, and WYKEHAM GLACIER SOUTH glaciers are derived from literature, as follows: ① Ciracì E, Velicogna I, Sutterley T C. Mass balance of Novaya Zemlya Archipelago, Russian High Arctic, using time-variable gravity from GRACE and altimetry data from ICESat and CryoSat-2 [J]. Remote Sensing, 2018, 10(11): 1817. ② Millan R, Mouginot J, Rignot E. Mass budget of the glaciers and ice caps of the Queen Elizabeth Islands, Canada, from 1991 to 2015 [J]. Environmental Research Letters, 2017, 12(2): 024016. Data for thirteen glaciers—McCall, West Gulkana, Eklutna, Wolverine, Alexander, Lemon Creek, Taku, Yuri, Andrei, MITTIVAKKAT, GEITLANDSJOKULL, AUSTRE BROEGGERBREEN, and ENGABREEN—are sourced from existing datasets. The specific sources are as follows: The existing datasets are from the Fluctuations of Glaciers (FoG) Database of the World Glacier Monitoring Service. This database is a standardized international collection of information on glacier conditions and changes (length, area, volume, mass), based on in-situ measurements, remote sensing, and reconstructions.\r\n
(4) Glacier velocity data originate from the ITS_LIVE (Inter-mission Time Series of Land Ice Velocity and Elevation) dataset, extracted from Landsat 4, 5, 7, and 8 satellite imagery. This dataset covers all land ice areas exceeding 5 km² in size, spanning the period from 1985 to 2022. This dataset offers resolutions of 120m and 240m. This dataset utilizes the 120m resolution velocity data generated from optical satellite imagery. Data retrieval website: (https://its-live.jpl.nasa.gov/).\r\n
(5) Glacier temperature data is sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Atmospheric Reanalysis (ERA5), a comprehensive reanalysis dataset. This dataset utilizes atmospheric reanalysis products to obtain air temperature data. Data acquisition website: (https://cds.climate.copernicus.eu/#!/home)", "ds_quality": "
(1) Glacier area uncertainty: This dataset only accounts for errors caused by the spatial resolution of Landsat remote sensing imagery. The calculation formula is as follows: ε = N*A. Here, ε represents the glacier area error (km²); N denotes the glacier perimeter length; A is the length of half a pixel (15 m).\r\n
(2) Model validation involves using field data to determine whether discrepancies between the model and measured data stem from overfitting. To this end, the research team removed 60% of the thickness data from the Alpine region for modeling and compared the results with the removed data. Results showed a discrepancy of −16 ± 51 meters between the model and measured data, indicating no overfitting. Error analysis across different ice thickness ranges revealed that ice layers thicker than 100 meters exhibited errors between 25% and 35%, while those thinner than 100 meters showed errors exceeding 50%.\r\n
(3) Mass balance data quality control follows the same standards as the data source; \r\n
(4) Glacier velocity: Refer to the ITS_LIVE data documentation (http://its-live-data.jpl.nasa.gov.s3.amazonaws.com/documentation/ITS_LIVE-Regional-Glacier-and-Ice-Sheet-Surface-Velocities.pdf) for relevant data quality and error calculations. \r\n
(5) For glacier temperature, related data quality, and error calculations, refer to (https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation)", "ds_ref_way": "", "ds_abstract": "
This dataset utilizes multi-source remote sensing data to generate information on Arctic glacier area, thickness, ice volume, mass balance, movement velocity, and surface temperature since 1980. A total of 16 representative glaciers within the study area were selected as research subjects, distributed as shown in Figure 1. The dataset was produced as follows: Based on Landsat data, visual interpretation methods were employed to generate five glacier inventories for the Arctic region covering the years 1985, 1995, 2005, 2015, and 2024, from which corresponding glacier areas were extracted. Using two publicly available glacier thickness and mass balance datasets, thickness and mass balance information for the target glaciers during the study period was obtained through mean value processing. Mass balance data were sourced from published literature and model reconstructions. Glacier movement velocities for the target glaciers from 1985 to 2022 were extracted using ITS_LIVE velocity data. Monthly temperatures for the target glaciers from 1985 to 2024 were determined using ERA5 reanalysis data. Glacier surface temperature ranges were delineated based on glacier inventories, with the average temperature within each range representing the surface temperature.", "ds_time_res": "", "ds_acq_place": "Arctic", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) Glacier Area. The ratio threshold method and snow cover index method based on optical remote sensing imagery are currently the most commonly used techniques for glacier boundary extraction. Given that some glacial areas in this study are covered by surface moraines and high-quality imagery is insufficient, we used the RGIV7 glacier catalog as a reference to accurately quantify glacier boundaries. This involved combining multiple feature parameters to obtain boundary ranges, followed by manual revision of the results in ArcGIS software. Finally, glacier area changes in this region were studied based on the manually revised results. During manual revision, snow cover and shadow effects should be minimized to ensure smooth glacier boundaries without jagged edges. Vector lines should closely follow actual glacier margins, avoiding large jumps at segment turning points.\r\n
(2) Glacier thickness and volume information. Glacier thickness and volume information: Glacier ice thickness estimation is based on the Shallow-Ice Approximation (SIA) model, which accounts for surface movement and bedding slip. The relationship between surface velocity and bedding velocity is calculated using a formula that incorporates ice density, gravitational acceleration, and the elevation difference between the ice surface and bedding. Bedding velocity is estimated by introducing a proportionality factor related to surface velocity. Ice thickness calculations account for the effects of velocity, ice surface slope, and density, ultimately providing estimated ice thickness values for a glacier network with connectivity less than 2 across all glaciers. Based on the acquired glacier thickness data, corresponding glacier volume is derived by multiplying thickness by area.\r\n
(3)The glacier mass balance is based on data collected from literature and does not involve processing methods.\r\n
(4) Glacier velocity acquisition. Based on IT_LIVE data and glacier flow line data extraction, the flow velocity of each glacier is extracted using the raster clipping tool in ArcGIS software, and its average value is calculated. \r\n
(5) Glacier temperature acquisition. Based on ERA5 data and glacier boundary data extraction, the temperature of each glacier is extracted using the raster clipping tool in ArcGIS software, and its average value is calculated.", "ds_ref_instruction": "" } }, "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": [ "冰川面积", "冰川厚度和储量", "冰川运动速度", "冰川物质平衡", "冰川温度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极" ], "ds_time_tags": [ 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, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "ds_managers": [ { "true_name": "吴通华", "email": "thuawu@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院，青藏高原冰冻圈研究站, 冰冻圈科学国家重点实验室, ", "country": "中国" } ], "category": "冰川" }