湖冰厚度对区域水文气候系统、湖泊生态系统及冰面人类活动均具有重要意义，且对全球变暖响应极为敏感。但受实地观测难度大、有效遥感观测手段匮乏制约，目前学界对湖冰厚度的时空变化规律仍认知不足。尽管依托大气环流模式数据驱动的湖冰模型已得到大量研发与应用，但全球湖冰厚度评估大多仅基于气候强迫数据网格内的理想湖泊开展模拟，缺乏对实际湖泊状况的刻画，因此亟需构建适用于真实湖泊的全球湖冰厚度估算方法。\n
本研究利用卫星测高数据，反演近三十年来北半球 16 个大型湖泊（贝加尔湖、大奴湖等）的湖冰厚度，反演精度约 0.2 米；继而构建以遥感数据为主要驱动的一维湖冰模型，并结合卫星测高湖冰厚度数据完成交叉验证，建立一套可靠的北半球面积 50km² 以上湖泊湖冰厚度估算方案。\n
该数据集版本1由三个产品组成，包括：（1）2002-2019 年卫星测高仪测量的 11 个大型湖泊的湖冰厚度；（2）2003-2018 年一维遥感湖冰模型模拟的北半球面积大于 50 平方公里的 1260 个湖泊的湖冰厚度和湖面积雪深度；（3）2091-2099 年湖冰模型模拟的未来湖冰厚度和湖面积雪深度，并修改了冰层生长模块。
\n版本2由四个文件组成，包括：（1）1992-2019 年卫星测高仪测量的 16 个大型湖泊的湖泊冰厚度（16 个大型湖泊的测高 LIT. xlsx）；（2）2003-2018 年一维遥感湖冰模型模拟的北半球面积大于 50 平方公里的 1313 个湖泊的每日湖冰厚度和湖面积雪深度（NetCDF 格式）；（3）湖冰模型利用改进的冰生长模块模拟的 2071-2099 年未来湖冰厚度和湖面积雪深度（表 S1.xlsx）；（4）包含湖泊 ID、名称、位置和面积的查找表。
", "ds_source": "通过Jason-1/2/3 号卫星生成湖泊冰层厚度的数据。
", "ds_process_way": "本数据集基于一种适用于脉冲限制型测高卫星的穿透算法，反演并分析 1992 年以来北半球 16 个大型湖泊与水库的湖冰厚度变化。这也是首次将脉冲限制型雷达测高数据应用于湖冰厚度反演，构建了目前时序跨度最长的遥感湖冰厚度数据集；受早期观测数据质量限制，部分中小型湖泊仅能获得 2002-2019 年的有效湖冰厚度序列。\n
该算法有效弥补了现有湖冰厚度实地观测数据稀缺的短板，可为湖冰模型的率定与验证提供重要支撑。为提升不同积雪覆盖条件下卫星测高湖冰厚度的估算精度，本研究进一步构建流程框架，将雷达回波信号划分为纯湖冰厚度信号、冰雪复合厚度信号与混合信号三类。\n
在此基础上，研究构建以遥感地表温度与反照率为核心驱动因子的湖冰模型，并利用上述 16 个大型湖库的卫星测高实测湖冰厚度完成模型验证。依托经验证模型，完成 2003-2018 年北半球 1313 个面积大于 50 平方千米湖库的湖冰厚度模拟，建立北半球湖冰厚度基准数据集。", "ds_quality": "
采用无需遥感驱动输入的简易湖冰模型，结合经遥感模型模拟结果率定后的参数，分析近三十年湖冰厚度演变特征，并预估本世纪末湖冰厚度对气候变化的响应规律，该简易模型与遥感驱动湖冰模型模拟结果一致性良好。
", "ds_acq_start_time": "2002-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "泽斯科耶湖、呼伦湖、贝加尔湖、霍夫斯戈尔湖、哈尔湖、萨西克科尔湖、贝克湖、阿萨巴斯卡湖、大奴湖、塔斯利纳湖、大熊湖", "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": 315432398, "ds_files_count": 10, "ds_format": ".nc、.xlsx", "ds_space_res": "", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "a1f327cc-7e2a-4521-a623-2e1ad89b9817.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-09-25 14:52:16", "last_updated": "2026-05-20 16:09:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB4023.2023", "i18n": { "en": { "title": "Lake ice thickness dataset", "ds_format": ".nc、.xlsx", "ds_source": "Generate data on lake ice thickness using the Jason-1/2/3 satellite.
", "ds_quality": "Using a simple lake ice model that does not require remote sensing driven input, combined with parameters calibrated by remote sensing model simulation results, the evolution characteristics of lake ice thickness in the past thirty years were analyzed, and the response law of lake ice thickness to climate change at the end of this century was estimated. The consistency between this simple model and the remote sensing driven lake ice model simulation results was good.
", "ds_ref_way": "", "ds_abstract": "The thickness of lake ice is of great significance to the regional hydroclimatic system, lake ecosystem, and human activities on the ice surface, and is extremely sensitive to global warming response. However, due to the difficulty of on-site observation and the lack of effective remote sensing observation methods, the academic community still lacks understanding of the spatiotemporal variation patterns of lake ice thickness. Although lake ice models driven by atmospheric circulation models have been extensively developed and applied, most global assessments of lake ice thickness are based solely on simulating ideal lakes within climate forcing data grids, lacking characterization of actual lake conditions. Therefore, there is an urgent need to construct a global estimation method for lake ice thickness applicable to real lakes.\r\n
This study uses satellite altimetry data to invert the ice thickness of 16 large lakes in the Northern Hemisphere (such as Lake Baikal and Lake Danu) over the past thirty years, with an inversion accuracy of approximately 0.2 meters; Subsequently, a one-dimensional lake ice model driven mainly by remote sensing data was constructed, and cross validation was completed by combining satellite elevation lake ice thickness data to establish a reliable estimation scheme for lake ice thickness in lakes with an area of over 50km ² in the Northern Hemisphere.\r\n
This dataset version 1 consists of three products, including: (1) the ice thickness of 11 large lakes measured by satellite altimeters from 2002 to 2019; (2) The lake ice thickness and snow depth of 1260 lakes with an area greater than 50 square kilometers in the Northern Hemisphere simulated by a one-dimensional remote sensing lake ice model from 2003 to 2018; (3) The future lake ice thickness and lake snow depth simulated by the 2091-2099 lake ice model, and the ice growth module was modified.
\r\nVersion 2 consists of four files, including: (1) Lake ice thickness measured by satellite altimeters from 1992 to 2019 for 16 large lakes (altimetry LIT. xlsx for 16 large lakes); (2) The daily lake ice thickness and lake snow depth (NetCDF format) of 1313 lakes in the Northern Hemisphere with an area greater than 50 square kilometers simulated by a one-dimensional remote sensing lake ice model from 2003 to 2018; (3) The lake ice model uses an improved ice growth module to simulate the future lake ice thickness and lake snow depth from 2071 to 2099 (Table S1. xlsx); (4) A lookup table containing lake ID, name, location, and area.
", "ds_time_res": "", "ds_acq_place": "Zeskoye Lake, Hulun Lake, Baikal Lake, Hovs Gol Lake, Hal Lake, Susikkor Lake, Baker Lake, Athabasca Lake, Great Slave Lake, Taslina Lake, Great Bear Lake", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The dataset version 1 consists of three products including: (1) Lake ice thickness of 11 large lakes measured by satellite altimeters for 2002-2019; (2) Lake ice thickness and lake surface snow depth of 1260 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018; (3) Future lake ice thickness and surface snow depth for 2091-2099 modeled by the lake ice model with a modified ice growth module.\r\n
\r\nVersion 2 consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation provided by this metadata in the reference section." } }, "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, "belong_to_nieer": false, "ds_topic_tags": [ "湖冰厚度", "湖面积雪深度", "卫星测高", "湖冰模型", "气候变化" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泽斯科耶湖", "呼伦湖", "贝加尔湖", "霍夫斯戈尔湖", "哈尔湖", "萨西克科尔湖", "贝克湖", "阿萨巴斯卡湖", "大奴湖", "塔斯利纳湖", "大熊湖" ], "ds_time_tags": [ 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "李兴东", "email": "lxd6304@126.com", "work_for": "清华大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "李兴东", "email": "lxd6304@126.com", "work_for": "清华大学", "country": "中国" } ], "ds_managers": [ { "true_name": "李兴东", "email": "lxd6304@126.com", "work_for": "清华大学", "country": "中国" } ], "category": "积雪" }