多年冻土分布图是寒区水文过程、生态系统稳定性及陆气相互作用研究的重要基础数据。然而，由于观测站点稀疏及地形高度异质性，塔里木河流域长期缺乏高分辨率的多年冻土数据。为此，本研究建立了塔里木河流域冻土分布制图的总体框架。首先，采用基于数字高程模型（DEM）校正的普通克里格插值方法对缺失的TRIMS LST进行重建；其次，通过将站点观测的日均地表温度（GST）与四个瞬时陆地表面温度（LST）观测值进行回归拟合建立 GST–LST 经验模型，从而估算整个流域的日均 GST，进一步计算地面冻结指数（DDF）和融化指数（DDT）；在此基础上，利用多年冻土顶层温度（TTOP）模型及rₖ因子进行冻土分布模拟；最后，利用阿尔金和西昆仑子区域调查图与钻孔数据对模拟的冻土图进行验证，并与广泛使用的北半球多年冻土产品（Obu图和冉图）进行比较。该数据集在塔里木河流域具有较高的空间精度，Kappa 系数在西昆仑和阿尔金子区分别达到 0.84 和 0.52，OA分别为0.97和0.87，显著优于对比产品。该数据包含多年冻土与季节冻土空间类型、冰川和湖泊等要素，覆盖塔里木河流域全域，空间分辨率为 1 km。与现有北半球多年冻土产品相比，本数据可为塔里木河流域多年冻土变化、冻融灾害风险评估、水文过程模拟及气候变化影响研究提供重要的数据支撑。", "ds_source": "
TRIMS LST产品来源于国家青藏高原科学数据中心（ https://doi.org/10.11888/Meteoro.tpdc.271252.）。该产品通过增强型的卫星热红外遥感-再分析数据集成方法制备而成。主要输入数据为Terra/Aqua MODIS LST产品和GLDAS等数据，辅助数据包括卫星遥感提供的植被指数、地表反照率等，充分利用了卫星热红外遥感和再分析数据提供的地表温度高频分量、低频分量以及地表温度的空间相关性，最终重建得到较高质量的全天候地表温度数据集。ESA 土地覆被数据来源于欧洲空间局（ESA）中分辨率土地覆盖气候变化倡议项目（CCI MRLC）（https://data.ceda.ac.uk/neodc/esacci/land_cover/data/pft/v2.0.8/）， 该数据集融合多源高分辨率辅助数据，构建了 1992—2020 年全球 14 类植物功能型逐年覆盖比例数据，有效降低了传统土地覆盖—PFT 转换带来的不确定性。数据提供逐年产品，时间范围为 1992—2020 年，空间分辨率为300m。Copernicus DEM数据由欧洲航天局发布，TanDEM-X任务期间获得全球雷达卫星数据(2011-2015年)，经过干涉处理等步骤得到TanDEM-X DEM产品，最终基于需要人工参与的半自动的编辑后得到的WorldDEM进行抽样得到30米格网间距的哥白尼30米分辨率DEM数据，可从https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM访问获取。", "ds_process_way": "
（1）利用ArcGIS10.8空间分析工具，对TRIMS LST进行数据拼接、裁剪、投影转换，投影坐标系为WGS84；\n
（2）在Python 语言环境下，采用岭回归方法建立 GST与 LST之间的统计拟合模型，并通过10折交叉验证进行优化，并用于后续区域GSTs计算；\n
（3）采用双线性方法将DEM数据重采样到1k m，采用众数法将ESA 土地覆被数据重采样到1km，使其与TRIMS LST的分辨率相匹配。\n
（4）多年冻土分布采用 TTOP模型进行反演计算，输出的多年冻土分布结果经二值分类（多年冻土/非多年冻土）处理后，形成 2005-2020 年塔里木河流域多年冻土分布数据产品。", "ds_quality": "
采用实地子区域（阿尔金、西昆仑）冻土调查图、野外钻孔数据、探地雷达实测数据以及现有北半球多年冻土图对模拟的塔里木河流域冻土图进行综合验证。通过二分类混淆矩阵计算总体精度（OA）与 Kappa 系数（k）对子区域冻土模拟精度进行量化评估。 验证结果表明：模拟的冻土空间分布与湿地调查图以及与现有大比例尺多年冻土图均具有较高一致性，多年冻土面积约为21.75×104 km2（占流域面积21.02%），季节冻土面积约为79.53×104 km2（占流域面积76.86%），不包括冰川（1.79×104 km2，占1.73%）和湖泊（0.41×104 km2，占0.39%）。多年冻土主要分布在流域的喀喇昆仑山、昆仑山和阿尔金山，以及帕米尔高原与天山东段。在西昆仑与阿尔金两子区与调查图的对比中，本研究产品的 k 值分别为 0.84 与 0.52，对应的总体精度（OA）分别为 0.97 与 0.87（两区平均 k ≈ 0.82），显著优于Obu图（k值分别为0.49和0.43）和冉图（k值分别为0.42和0.02）。钻孔资料表明，阿尔金稳定与不稳定多年冻土共存，而在模拟图中，这两类多年冻土均被正确识别为多年冻土，同时季节冻土分布也能较为准确识别。时序检验显示 2005–2020 年冻土退化主要发生在多年冻土边缘与不连续多年冻土带，其变化趋势与区域气温升高一致。因此，本数据集在塔里木河流域多年冻土区具有较高的空间与时间可信度，可作为区域冻土演变、水文与生态响应研究的可靠基础数据。", "ds_acq_start_time": "2005-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "塔里木河流域", "ds_acq_lon_east": 93.81, "ds_acq_lat_south": 34.34, "ds_acq_lon_west": 73.56, "ds_acq_lat_north": 43.53, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2465910, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "1km", "ds_coordinate": "krasovsky1940", "ds_projection": "D_Krasovsky_1940", "ds_thumbnail": "914f64a5-85ee-4f71-8d80-100aca73022e.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据详细信息参见：Zhang, G., Yuan, Z., Hu, L., Cao, Z., & Nan, Z. (2025). Permafrost mapping of the Tarim Basin based on TTOP model for 2005–2020. GIScience & Remote Sensing, 62(1). https://doi.org/10.1080/15481603.2025.2596941", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "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": "2025-12-09 10:50:20", "last_updated": "2025-12-09 15:45:00", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PERAMCARBON_TARIM.DB7026.2025", "i18n": { "en": { "title": "Permafrost map of the Tarim Basin for 2005–2020", "ds_format": "*.tif", "ds_source": "
The TRIMS LST product was obtained from the National Tibetan Plateau Data Center (https://doi.org/10.11888/Meteoro.tpdc.271252\n.). This product was generated using an enhanced satellite thermal infrared remote sensing–reanalysis data fusion approach. The primary input datasets include the Terra/Aqua MODIS LST products and GLDAS reanalysis data, while auxiliary inputs consist of satellite-derived vegetation indices, surface albedo, and other surface parameters. By fully exploiting the high-frequency and low-frequency components of satellite thermal infrared observations as well as the spatial autocorrelation of land surface temperature, this method reconstructs a high-quality, all-weather LST dataset with improved spatiotemporal continuity and consistency.\nThe ESA land cover data were obtained from the European Space Agency (ESA) Climate Change Initiative Medium Resolution Land Cover project (CCI MRLC) (https://data.ceda.ac.uk/neodc/esacci/land_cover/data/pft/v2.0.8/\n). By integrating multiple high-resolution auxiliary datasets, this product provides annual fractional cover estimates of 14 global plant functional types (PFTs) from 1992 to 2020, thereby effectively reducing the uncertainty associated with traditional land cover–to–PFT conversion approaches. The dataset is provided at an annual temporal resolution with a spatial resolution of 300 m.\nThe Copernicus DEM was released by the European Space Agency and is based on global radar satellite observations acquired during the TanDEM-X mission (2011–2015). After interferometric processing and subsequent semi-automatic manual editing, the WorldDEM product was generated and further resampled to produce the Copernicus DEM at a spatial resolution of 30 m. The dataset is publicly available from the Copernicus Data Space (https://dataspace.copernicus.eu/explore-data/data-collections/copernicus-contributing-missions/collections-description/COP-DEM\n).", "ds_quality": "
The simulated permafrost map of the Tarim Basin was comprehensively validated using in situ subregional permafrost survey maps (Aerjin and West Kunlun), field borehole data, ground-penetrating radar measurements, and existing Northern Hemisphere permafrost maps. The accuracy of the subregional permafrost simulations was quantitatively assessed using overall accuracy (OA) and the Kappa coefficient (k) derived from a binary confusion matrix.The validation results indicate that the simulated permafrost spatial distribution is highly consistent with both wetland survey maps and existing large-scale permafrost maps. The area of permafrost was estimated to be approximately 21.75×104 km2 (21.02% of the basin), while seasonal frozen ground covered about 79.53 × 104 km2 (76.86%), excluding glaciers (1.79 × 104 km2, 1.73%) and lakes (0.41 × 104 km2, 0.39%). Permafrost is mainly distributed in the Karakoram, Kunlun, and Aerjin Mountains, as well as the Pamir Plateau and the eastern section of the Tianshan Mountains. Comparisons with survey maps in the West Kunlun and Aerjin subregion yielded k values of 0.84 and 0.52, with corresponding OAs of 0.97 and 0.87 (average k ≈ 0.82), significantly outperforming the Obu map (k = 0.49 and 0.43) and the Ran map (k = 0.42 and 0.02). Borehole data indicate the coexistence of stable and unstable permafrost in the Aerjin region; both types were correctly identified as permafrost in the simulation, and seasonally frozen ground was also reasonably well captured. Temporal analysis from 2005 to 2020 shows that permafrost degradation primarily occurred at the margins of continuous permafrost and in discontinuous permafrost zones, consistent with regional warming trends.Therefore, this dataset demonstrates high spatial and temporal reliability in permafrost regions of the Tarim Basin and provides a robust foundation for studies on regional permafrost dynamics, hydrological processes, and ecological responses", "ds_ref_way": "", "ds_abstract": "
Permafrost distribution maps are fundamental datasets for studying hydrological processes, ecosystem stability, and land–atmosphere interactions in cold regions. However, due to the sparse distribution of observation stations and the high heterogeneity of terrain, high-resolution permafrost data for the Tarim Basin have long been lacking. To address this gap, this study established an overall framework for mapping permafrost distribution in the Tarim Basin. First, missing TRIMS LST data were reconstructed using ordinary kriging interpolation corrected by a digital elevation model (DEM). Second, an empirical GST–LST model was developed by regressing daily mean ground surface temperature (GST) observed at stations against four instantaneous LST observations, enabling the estimation of daily mean GST across the basin and the subsequent calculation of the degree of freezing (DDF) and thawing (DDT) indices. Based on these results, permafrost distribution was simulated using the TTOP model combined with the rₖ factor. Finally, the simulated permafrost maps were validated using subregional survey maps and borehole data from the Aerjin and Western Kunlun subregions and compared with widely used Northern Hemisphere permafrost products (Obu and Ran maps).The resulting dataset exhibits high spatial accuracy within the Tarim Basin, with Kappa coefficients of 0.84 and 0.52 and overall accuracies (OA) of 0.97 and 0.87 in the Western Kunlun and Aerjin subregions, respectively, significantly outperforming the reference products. The dataset includes spatial types of permafrost and seasonally frozen ground, as well as glaciers and lakes, covering the entire Tarim Basin at a spatial resolution of 1 km. Compared with existing Northern Hemisphere permafrost products, this dataset provides essential data support for studies of permafrost dynamics, freeze–thaw hazard assessment, hydrological modeling, and climate change impacts in the Tarim Basin.
", "ds_time_res": "1km", "ds_acq_place": "Tarim Basin", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "（1）Using ArcGIS 10.8 spatial analysis tools, the TRIMS LST data were mosaicked, clipped, and projected, with the projection coordinate system set to WGS84.（2）In the Python programming environment, a statistical fitting model between GST and LST was established using ridge regression. The model was optimized via 10-fold cross-validation and subsequently applied to calculate regional GSTs.（3）DEM data were resampled to a 1 km resolution using bilinear interpolation, and ESA land cover data were resampled to 1 km using the mode method to match the spatial resolution of TRIMS LST.（4）The distribution of permafrost was derived using the TTOP model. The resulting permafrost map was binarized (permafrost/seasonally frozen ground) to generate a 2005–2020 permafrost distribution dataset for the Tarim Basin.", "ds_ref_instruction": "Data details can be found at:Zhang, G., Yuan, Z., Hu, L., Cao, Z., & Nan, Z. (2025). Permafrost mapping of the Tarim Basin based on TTOP model for 2005–2020. GIScience & Remote Sensing, 62(1). https://doi.org/10.1080/15481603.2025.2596941" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "塔里木流域", "多年冻土", "TTOP模型", "遥感", "气候变化" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "塔里木河流域" ], "ds_time_tags": [ 2005, 2020 ], "ds_contributors": [ { "true_name": "张国飞", "email": "zhangguofei@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "张国飞", "email": "zhangguofei@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" }, { "true_name": "袁在武", "email": "yuanzw2024@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "ds_managers": [ { "true_name": "张国飞", "email": "zhangguofei@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "冻土" }