{
    "created": "2025-11-21 17:14:18",
    "updated": "2026-05-27 10:01:31",
    "id": "8d3fa175-e7ae-4dab-99ef-6249e36a3922",
    "version": 8,
    "ds_topic": null,
    "title_cn": "格陵兰岛出口冰川的冰川前缘位置：用于算法验证的空间扩展季节性记录与基准数据集（2002–2021年）",
    "title_en": "Ice front positions for Greenland glaciers (2002–2021): a spatially extensive seasonal record and benchmark dataset for algorithm validation",
    "ds_abstract": "<p>&emsp;&emsp;格陵兰冰川冰锋位置的变化，能够反映冰川进退的幅度与趋势，对研究冰盖动力学机理至关重要。本数据集完整收录了格陵兰全域冰川末端位置的大范围季节性观测记录，涵盖海洋型、湖泊型、陆基型等多类冰川，时间跨度为 2002—2021 年，共计19171 条冰锋解译数据。\n<p>&emsp;&emsp;数据基于多源卫星影像提取，数据源包括陆地卫星 Landsat 5/7/8、哨兵卫星 Sentinel-1/2、中分辨率成像光谱仪 MODIS、欧洲环境卫星合成孔径雷达 ENVISAT ASAR、先进星载热发射和反射辐射计 ASTER 以及欧洲遥感卫星合成孔径雷达 ERS SAR。\n<p>&emsp;&emsp;所有崩解冰锋位置均借助 GEEDiT、ENVI、ArcGIS、QGIS 等软件，按照标准化流程完成矢量化解译，并配套完整元数据。研究将本数据集与两套主流产品开展质量校验：一是人工解译冰川末端轨迹数据集 TermPicks（多传感器、多冰川环境下规模最大的人工数据集），二是时间分辨率更高的自动化解译产品 AutoTerm。\n<p>&emsp;&emsp;校验结果显示，本数据与 TermPicks 的平均最小偏差距离为 86 米，与 AutoTerm 的平均最小偏差距离为 115 米，证明冰锋定位精度优异。\n<p>&emsp;&emsp;该数据集采用 GeoPackage 格式发布，总大小 26.5 MB，同时附带各冰川中线数据，可供深度分析使用。数据集空间覆盖广、质量可靠，可作为基准数据，用于格陵兰冰川动力学研究、优化冰盖模型的时变边界条件，以及支撑冰川冰锋自动追踪算法的研发与验证。",
    "ds_source": "",
    "ds_process_way": "",
    "ds_quality": "",
    "ds_acq_start_time": "2002-01-01 00:00:00",
    "ds_acq_end_time": "2021-12-31 00:00:00",
    "ds_acq_place": "格陵兰",
    "ds_acq_lon_east": null,
    "ds_acq_lat_south": null,
    "ds_acq_lon_west": null,
    "ds_acq_lat_north": null,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "open-access",
    "ds_total_size": 8889596,
    "ds_files_count": 2,
    "ds_format": "GeoPackage",
    "ds_space_res": "",
    "ds_time_res": "年",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "8d3fa175-e7ae-4dab-99ef-6249e36a3922.png",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf",
    "ds_serv_man": "李红星",
    "ds_serv_phone": "0931-4967592",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "",
    "subject_codes": [
        "170.45",
        "170.55"
    ],
    "quality_level": 3,
    "publish_time": "2025-11-27 15:50:46",
    "last_updated": "2026-05-26 17:24:35",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ZENODO.DB7024.2025",
    "i18n": {
        "en": {
            "title": "Ice front positions for Greenland glaciers (2002–2021): a spatially extensive seasonal record and benchmark dataset for algorithm validation",
            "ds_format": "GeoPackage",
            "ds_source": "",
            "ds_quality": "",
            "ds_ref_way": "",
            "ds_abstract": "<p>&emsp;Variations in Greenland glacier ice-front positions reflect the magnitude and trends of glacier advance and retreat and are critical for understanding ice-sheet dynamics. This dataset provides a spatially extensive and seasonally targeted record of glacier terminus positions across Greenland. It includes 19,171 ice-front delineations for multiple glacier types, including marine-, lake-, and land-terminating glaciers, spanning the period 2002–2021. The delineations were derived from multi-source satellite imagery, including Landsat 5/7/8, Sentinel-1/2, MODIS, ENVISAT ASAR, ASTER, and ERS SAR.\r\n<p>&emsp;All calving front positions were digitized using standardized workflows implemented in GEEDiT (Lea, 2018), ENVI, ArcGIS, and QGIS, and are accompanied by detailed metadata. Data quality was assessed through comparisons with TermPicks (Goliber et al., 2022), the largest compilation of manually interpreted glacier terminus traces across diverse sensors and glacier conditions, and AutoTerm (Zhang et al., 2023), an automated product with denser temporal coverage. Mean average minimum distances (AMD) of 86 m relative to TermPicks and 115 m relative to AutoTerm indicate high position precision.\r\n<p>&emsp;The dataset is distributed in GeoPackage format (total size: 26.5 MB) and includes glacier-specific centrelines for analysis. It provides a spatially extensive, high-quality benchmark for investigating Greenland glacier dynamics, refining time-varying boundary conditions in ice-sheet models, and supporting the development and validation of automated glacier front-tracking algorithms.",
            "ds_time_res": "",
            "ds_acq_place": "Greenland",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "",
            "ds_ref_instruction": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "recommendation_value": 0,
    "license_type": "https://creativecommons.org/licenses/by/4.0/",
    "doi_reg_from": "reg_outside",
    "cstr_reg_from": "reg_outside",
    "doi_not_reg_reason": null,
    "cstr_not_reg_reason": null,
    "is_paper_in_submitting": false,
    "ds_topic_tags": [
        "格陵兰",
        "冰川",
        "冰川冰锋"
    ],
    "ds_subject_tags": [
        "地理学",
        "水文学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "格陵兰"
    ],
    "ds_time_tags": [
        2002,
        2003,
        2004,
        2005,
        2006,
        2007,
        2008,
        2009,
        2010,
        2011,
        2012,
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019,
        2020,
        2021
    ],
    "ds_contributors": [
        {
            "true_name": "江利明",
            "email": "jlm@whigg.ac.cn",
            "work_for": "中国科学院精密测量科学与技术创新研究院",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "江利明",
            "email": "jlm@whigg.ac.cn",
            "work_for": "中国科学院精密测量科学与技术创新研究院",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "江利明",
            "email": "jlm@whigg.ac.cn",
            "work_for": "中国科学院精密测量科学与技术创新研究院",
            "country": "中国"
        }
    ],
    "category": "冰川"
}