活动层厚度（ALT）的冻土对气候和人类活动响应的重要指标。然而，由于缺乏时空连续的高空间分辨率活动层厚度数据集，本研究基于随机森林降尺度模型将MOD11 L2地表温度数据和 MOD09GQ NDVI降尺度，并根据stefan 公式求算了2000-2024年大通河上游包括木里地区的30米空间分辨率活动层厚度数据
", "ds_source": "MOD11 L2地表温度数据和 MOD09GQ NDVI数据来源于NASA的earth dataserch 引擎，Landsat数据来源于USGS的earthexplore 引擎。30米 TanDEM数字高程模型来自DLR，30m SRTM DEM来自USGS的earthexplore 引擎。实测活动层数据来源于已发表文献，详见文章。
", "ds_process_way": "1）利用随机森林算法，结合30m数字高程模型与LandsatNDVI数据对MOD09 GQ NDVI 数据进行降尺度；\n
2）结合降尺度后的NDVI数据，DEM等对MOD1 L2温度数据进行降尺度。\n
3）从已有大通河上游（及研究区）活动层实测数据筛选在均匀地区测量的活动层数据，构建活动层厚度-地表积温之间的关系系数；\n
4）基于活动层厚度-及问数据，结合基于降尺度的地表温度数据求算2000-2024年间大通河上游每年活动层厚度。
", "ds_quality": "降尺度后的地表温度数据RMSE为4.48度，均值为-0.009度，30米空间分辨率的活动层厚度RMSE为0.5m，mean为-0.25米
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "大通河源头", "ds_acq_lon_east": 99.96166666666667, "ds_acq_lat_south": 37.77722222222222, "ds_acq_lon_west": 98.86666666666666, "ds_acq_lat_north": 38.331388888888895, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 1570040112, "ds_files_count": 0, "ds_format": "*.tif", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Latlon", "ds_thumbnail": "81a7a27a-d6be-4eb5-9611-3e9c00863c87.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据详细信息参见：Zhang S. et al.(2025). The response of alpine permafrost to decadal human disturbance in the context of climate warming", "ds_from_station": "", "organization_id": "5b99d600-008a-4069-8fc3-7adb9c3f2f8b", "ds_serv_man": "张淑萍", "ds_serv_phone": "13107507861", "ds_serv_mail": "zhangshuping@nwpb.cas.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-06-11 12:04:17", "last_updated": "2026-06-11 12:04:17", "protected": false, "protected_to": "2027-10-01 00:00:00", "lang": "zh", "cstr": "", "i18n": { "en": { "title": "30m active horizon data set at the source of Datong River (2000-2024)", "ds_format": "*.tif", "ds_source": "The MOD11 L2 surface temperature data and MOD09GQ NDVI data are sourced from NASA's Earth dataserch engine, while the Landsat data is sourced from USGS's Earthexplore engine. The 30 meter TanDEM digital elevation model is from DLR, and the 30 meter SRTM DEM is from USGS's Earthexplore engine. The measured activity layer data is sourced from published literature, please refer to the article for details.
", "ds_quality": "The RMSE of the downscaled surface temperature data is 4.48 degrees, with a mean of -0.009 degrees. The RMSE of the active layer thickness with a spatial resolution of 30 meters is 0.5m, with a mean of -0.25 meters
", "ds_ref_way": "", "ds_abstract": "The thickness of the active layer (ALT) of frozen soil is an important indicator of its response to climate and human activities. However, due to the lack of a spatially and temporally continuous high spatial resolution active layer thickness dataset, this study used a random forest downscaling model to downscale MOD11 L2 surface temperature data and MOD09GQ NDVI, and calculated the 30 meter spatial resolution active layer thickness data of the upper reaches of the Datong River, including the Muli area, from 2000 to 2024 using Stefan's formula
", "ds_time_res": "", "ds_acq_place": "Source of Datong River", "ds_space_res": "", "ds_projection": "", "ds_process_way": "1) Using the random forest algorithm and combining the 30m digital elevation model with Landsat NDVI data to downscale the MOD09 GQ NDVI data; 2) Combining downscaled NDVI data, DEM, etc. to downscale MOD1 L2 temperature data. 3) Select active layer data measured in uniform areas from existing measured data in the upper reaches of the Datong River (and research area), and construct the relationship coefficient between active layer thickness and surface accumulated temperature; 4) Based on the thickness of the active layer and data, combined with downscaled surface temperature data, calculate the annual thickness of the active layer in the upper reaches of the Datong River from 2000 to 2024.
", "ds_ref_instruction": "For detailed data information, please refer to: Zhang S. et al.(2025). The response of alpine permafrost to decadal human disturbance in the context of climate warming" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 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, "belong_to_nieer": false, "ds_topic_tags": [ "活动层厚度", "长时序", "高空间分辨率" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "大通河源头" ], "ds_time_tags": [ 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": "张淑萍", "email": "zhangshuping@nwpb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" }, { "true_name": "陈继", "email": "chenji@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": "霍俐君", "email": "huolijun@nwipb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" }, { "true_name": "李欣阳", "email": "lixinyang@nwipb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" }, { "true_name": "吴承颖", "email": "wuchengying@nwipb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" }, { "true_name": "张虎才", "email": "zhanghc@ynu.edu.cn", "work_for": "云南大学", "country": "中国" }, { "true_name": "冯起", "email": "qifeng@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": "张淑萍", "email": "zhangshuping@nwpb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "张淑萍", "email": "zhangshuping@nwpb.cas.cn", "work_for": "中国科学院西北高原生物研究所", "country": "中国" } ], "category": "冻土" }