多年冻土区的活动层厚度及年平均地是多年冻土变化研究的重要指标，在当前全球变暖的大背景之下，尤其在高海拔以及高纬度地区，气温变暖的速率还要高于其他地区，这对于多年冻土区的活动层厚度的变化必然会造成很大影响。在以往的研究中，基于野外实测数据以及遥感反演算法已经对活动层厚度在历史时期下的变化有了很多探讨，但其空间分辨率也较低。以CMIP6数据为基础，通过降尺度的方法获得1km分辨率的多年冻土区地表气温；然后该数据作为多年冻土活动层厚度未来变化的高精度高分辨输入变量，从而进一步获得1km分辨率的北半球活动层厚度，通过使用多种机器学习方法，获取多种方法多模式平均，活动层厚度数据集的精度和分辨率都得到提升。集合平均模式输出结果的RMSE为67.39 cm，MAE为44.39cm，R为0.74。
", "ds_source": "北半球多年冻土区活动层厚度观测数据建立在全球多年冻土网络（GTN-P）数据库（gtnpdatabase.org）的基础上，同时，我们还扩展了GTN-P数据，并从相关文献中添加更多的ALT观测数据。其它相关数据请参考Jin et al., 2024.
", "ds_process_way": "使用的机器学习模型包括逻辑回归（LR）、随机森林（RF）和LightGBM（LGB）。考虑到使用单一方法可能会导致模拟的过度拟合，我们在模拟ALT时使用了上述三种方法的集和平均结果。使用TDD、FDD、叶面积指数、降水量、积雪、太阳辐射、土壤含水量、土壤有机质、砾石的体积含量作为输入变量，与站点观测值相对应，用90%的数据作为测试集进行模型建立，10%的数据作为验证集对模型的精度进行验证评价，为了减少单次运行的不确定性，本研究对使用的三种机器学习模型运行了100次，用其集合平均结果，最终结合各时期的环境变量模拟出不同时期的多年冻土区活动层厚度的分布变化。
", "ds_quality": "数据质量良好
", "ds_acq_start_time": "1850-01-01 00:00:00", "ds_acq_end_time": "2100-12-31 00:00:00", "ds_acq_place": "northern hemisphere", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 0.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 76333114805, "ds_files_count": 2, "ds_format": "netcdf格式，单位cm", "ds_space_res": "1公里", "ds_time_res": "逐年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "5775b147-f7d8-42de-8b3e-065fe94ba286.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "9de89acc-5714-4927-aba3-ac88067dff8a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2024-03-01 11:45:26", "last_updated": "2025-06-24 14:27:34", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.2022YFF.DB6392.2024", "i18n": { "en": { "title": "Long-term series kilometer-level active layer thickness data set in Northern Hemisphere (1850-2100)", "ds_format": "", "ds_source": "Observations of active layer thickness (ALT) in the permafrost zone of the Northern Hemisphere are based on the Global Permafrost Network (GTN-P) database (gtnpdatabase.org), and we have also extended the GTN-P data and added more ALT observations from the relevant literature. For other relevant data, please refer to Jin et al., 2024.
", "ds_quality": "Good quality of data
", "ds_ref_way": "", "ds_abstract": "The active layer thickness is one important indicators for the permafrost study . Under the current global warming background, the rate of temperature warming is higher than that in other areas, especially in high altitude and high latitude areas. This will inevitably have a great impact on the change of the active layer thickness in the permafrost area. In previous studies, based on field measured data and remote sensing inversion algorithms, many discussions have been made on the changes in the active layer thickness in historical periods, but their spatial resolution is low. Based on the CMIP6 data, the surface air temperature in the permafrost area with a resolution of 1 km is obtained by downscaling; then the data is used as a high-precision and high-resolution input variable for the future changes in the active layer thickness, so as to further obtain the active layer thickness of the Northern Hemisphere with a resolution of 1 km. By using a variety of machine learning methods and obtaining multi-method multi-mode averages, the accuracy and resolution of the active layer thickness dataset are improved. The RMSE of the ensemble average model output result is 67.39 cm, the MAE is 44.39 cm, and the R is 0.74.
", "ds_time_res": "逐年", "ds_acq_place": "northern hemisphere", "ds_space_res": "1公里", "ds_projection": "", "ds_process_way": "The machine learning models used include logistic regression (LR), random forest (RF), and LightGBM (LGB). Considering that using a single method may lead to overfitting in the simulation, we used the set and average results of the three methods mentioned above when simulating ALT. Using TDD, FDD, leaf area index, precipitation, snow cover, solar radiation, soil moisture content, soil organic matter, and gravel volume content as input variables, corresponding to station observations, the model was established with 90% of the data as the test set and 10% of the data as the validation set to evaluate the accuracy of the model. In order to reduce the uncertainty of a single run, this study ran the three machine learning models 100 times and used their ensemble average results to simulate the distribution changes of active layer thickness in permafrost regions at different periods by combining environmental variables at each period", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "多年冻土", "遥感", "地表气温" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北半球" ], "ds_time_tags": [ 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 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, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2030, 2031, 2032, 2033, 2034, 2035, 2036, 2037, 2038, 2039, 2040, 2041, 2042, 2043, 2044, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100 ], "ds_contributors": [ { "true_name": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "金浩东", "email": "jinhd20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "赵国辉", "email": "zhgh@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": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "金浩东", "email": "jinhd20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "彭小清", "email": "pengxq@lzu.edu.cn", "work_for": "兰州大学资源环境学院 ", "country": "中国" }, { "true_name": "金浩东", "email": "jinhd20@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "冻土" }