本数据集为青藏高原多年冻土区活动层体积含水量空间分布产品，面向冻土水热过程、高寒水文与生态环境研究需求生产，是区域首套覆盖完整冻土区的高分辨率活动层水分数据集，填补了青藏高原全深度活动层水分连续空间制图的数据空白。\n
数据以2009-2024 年青藏高原冻土区 342 个实测点为基础，含环刀法实测剖面数据与探地雷达（GPR）反演数据；协同多源遥感因子（Sentinel‑1 反演、NDVI、NDWI、地表温度 LST）、SRTM 90m 地形因子、中国陆面模拟土壤属性数据集（CSDLv2）等公共产品建模生成。\n
采用四种集成机器学习模型融合（随机森林、极端随机树、XGBoost、CatBoost），基于 SHAP 递归特征消除优化特征组合，通过五折交叉验证保障模型泛化能力，并采用偏差感知多模型融合策略校正低值高估、高值低估的系统偏差，最终得到全域连续的活动层水分空间分布。\n
数据为单波段 GeoTIFF 栅格，代表多年平均融化鼎盛期（9-10 月）活动层体积含水量，单位 m³・m⁻³；空间范围为青藏高原冻土区，地理坐标系 GCS_WGS_1984，空间分辨率 90m；时间属性为多年综合静态断面，无时间周期；文件命名规范清晰，便于批量调用与识别。\n
数据质量经交叉验证，决定系数 R² 为 0.59-0.61，均方根误差 RMSE≈0.08 m³・m⁻³，平均绝对误差 MAE≈0.06 m³・m⁻³，质量控制覆盖样本筛选、异常值剔除、特征优选、模型融合与偏差校正全流程，空间格局可靠。\n
本数据集优势为90m 高分辨率、冻土全域覆盖、聚焦全活动层深度，相比传统表层土壤水分产品更适配冻土水热模拟；可应用于冻土变化监测、高寒区水文过程模拟、生态水文评估、气候响应研究与陆面模型数据同化等场景。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2009-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "青藏高原,多年冻土区", "ds_acq_lon_east": 68.02444444444444, "ds_acq_lat_south": 25.81388888888889, "ds_acq_lon_west": 104.6825, "ds_acq_lat_north": 39.805, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 3164851555, "ds_files_count": 2, "ds_format": "GeoTIFF", "ds_space_res": "90m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "地理坐标系", "ds_thumbnail": "af40fdfc-84b9-4dbb-8ed5-2cadcf400964.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "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", "170.5520" ], "quality_level": 0, "publish_time": "2026-04-29 15:17:29", "last_updated": "2026-05-30 15:55:20", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.PERMAFROST.DB7312.2026", "i18n": { "en": { "title": "90 m Active Layer Moisture Dataset for the Qinghai–Tibet Plateau Permafrost Region（2009-2024）", "ds_format": "GeoTIFF", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
This dataset is a spatial distribution product of the volumetric moisture content of the active layer in the permafrost region of the Qinghai Tibet Plateau. It is produced to meet the research needs of permafrost hydrothermal processes, alpine hydrology, and ecological environment. It is the first high-resolution active layer moisture dataset in the region to cover the entire permafrost region, filling the data gap in continuous spatial mapping of active layer moisture at all depths on the Qinghai Tibet Plateau.\r\n
The data is based on 342 measured points in the permafrost region of the Qinghai Tibet Plateau from 2009 to 2024, including profile data measured by the ring knife method and ground penetrating radar (GPR) inversion data; Collaborative multi-source remote sensing factors (Sentinel-1 inversion NDVI、NDWI、 Modeling and generation of public goods such as surface temperature LST, SRTM 90m terrain factor, and China Land Surface Simulation Soil Attribute Dataset (CSDLv2).\r\n
Four integrated machine learning models (random forest, extreme random tree, XGBoost, CatBoost) were used for fusion, and SHAP recursive feature elimination was used to optimize feature combination. Five fold cross validation was used to ensure the model's generalization ability, and a bias aware multi model fusion strategy was adopted to correct system biases with low overestimation and high underestimation. Finally, a continuous spatial distribution of active layer moisture was obtained for the entire domain.\r\n
The data is a single band GeoTIFF grid, representing the annual average volume water content of the active layer during the peak melting period (September to October), in m ³ · m ⁻ ³. The spatial range is the permafrost region of the Qinghai Tibet Plateau, with a geographic coordinate system of GCSWGS_1984 and a spatial resolution of 90m. The time attribute is a comprehensive static cross-section for many years, without a time period; The file naming convention is clear and easy to batch call and identify.\r\n
The data quality has been cross validated, with a determination coefficient R ² of 0.59-0.61, root mean square error RMSE ≈ 0.08 m ³ · m ⁻ ³, and average absolute error MAE ≈ 0.06 m ³ · m ⁻ ³. Quality control covers the entire process of sample screening, outlier removal, feature selection, model fusion, and deviation correction, and the spatial pattern is reliable.\r\n
The advantage of this dataset is its 90m high resolution, full coverage of frozen soil, and focus on the depth of the entire active layer. Compared to traditional surface soil moisture products, it is more suitable for simulating frozen soil water and heat; It can be applied to monitoring permafrost changes, simulating hydrological processes in high-altitude regions, ecological hydrological assessment, climate response research, and land surface model data assimilation.", "ds_time_res": "", "ds_acq_place": "Qinghai-Tibet Plateau,permafrost region", "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_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": [ "青藏高原", "多年冻土区", "活动层水分", "机器学习" ], "ds_subject_tags": [ "自然地理学", "水文地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "青藏高原", "多年冻土区" ], "ds_time_tags": [ 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "杜二计", "email": "duerji@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": "杜二计", "email": "duerji@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_managers": [ { "true_name": "杜二计", "email": "duerji@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }