祁连山国家公园高分辨率土壤有机碳密度（0~100cm）数据
", "ds_source": "本数据集的制作共使用祁连山及其周边区域973个土壤采样点实测数据，数据主要来源如下：\n（1）研究团队承担的国家自然科学基金委项目“祁连山土壤有机碳沿梯度变化规律及机理研究”（41771252）、“黑河流域生态水文样带调查”（91025002）、“干旱内陆河流域典型生态系统土壤碳模拟研究”（31270482），甘肃省重大项目“祁连山涵养水源生态系统与水文过程相互作用及其对气候变化的适应研究”（18JR4RA002）、甘肃省林草局科技创新项目“祁连山国家公园（甘肃片区）生态治理成效评估研究”（GYCX[2020]01）、甘肃省科技计划资助项目“甘肃省祁连山生态环境研究中心”（18JR2RA026）等项目数据。\n（2）国家冰川冻土沙漠科学数据中心和青藏高原科学数据中心公开发表的土壤剖面原始数据、文献集成数据（Xu et al., 2018 DOI:10.1038/s41598-018-20764-9）、世界土壤参比与信息中心（ISRIC）WoSIS土壤剖面数据库（https://www.isric.org/explore/wosis）等。原始数据经过质量控制，去除经纬度定位精度不足0.001的采样点，以及土壤有机碳含量与环境本底明显偏离的异常值。
", "ds_process_way": "根据“Scorpan”（Soils, Climate, Organisms, Relief, Parent material, Age, Geographic position）框架，使用数字土壤制图（Digital Soil Mapping, DSM）方法和基于瓦片结构的运算，模拟了祁连山地区0~100 cm土层10m分辨率土壤有机碳密度空间分布。预测方法主要是基于机器学习中的极端梯度提升算法（XGBoost，eXtreme Gradient Boosting），环境协变量数据包括：Sentinel-2多光谱影像、Sentinel-1雷达影像、气温、降水、辐射、地形、植被指数、位置等栅格数据。\n在R语言中构建数字土壤制图框架，为了减少内存占用量和提高计算速度，将整个祁连山地区划分为33个150km×150km的子区域（region），每个子区域内构建基于瓦片（瓦片大小：15km×15km）和并行计算的制图流程，通过bootstrap法重复建模，对每个bootstrap样本进行空间建模，得到建模结果的频率分布，建模的不确定性用标准差（sd）表示。
", "ds_quality": "经30次10折交叉验证表明，模型的RMSE和R2分别为6.26 kg/m2和 0.75。最终数据产品共分为33个子区域（region），mean和sd分别表示30次重复建模的均值和标准差，单位为kg/m2，表示0~100cm土层内单位面积上土壤有机碳的质量，各子区域合并后获得整个祁连山地区土壤有机碳密度均值（mean）和标准差（sd），分辨率为10m，数据量72.40GB，利用祁连山国家公园边界进行掩膜，采用bilinear法分别重采样至30m/90m/250m/500m/1000m，总数据量13.70GB。
", "ds_acq_start_time": "2010-06-01 00:00:00", "ds_acq_end_time": "2020-09-30 00:00:00", "ds_acq_place": "祁连山国家公园", "ds_acq_lon_east": 105.0, "ds_acq_lat_south": 43.0, "ds_acq_lon_west": 92.0, "ds_acq_lat_north": 35.0, "ds_acq_alt_low": 900.0, "ds_acq_alt_high": 5500.0, "ds_share_type": "open-access", "ds_total_size": 14727500562, "ds_files_count": 26, "ds_format": "GeoTiff", "ds_space_res": "10m/30m/90m/250m/500m/1000m", "ds_time_res": "年代", "ds_coordinate": "WGS84", "ds_projection": "无", "ds_thumbnail": "44bb9330-b68c-4d87-aa41-3234d77f4fca.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "8fa8eec3-a891-44cd-b60e-f6af19a16bba", "ds_serv_man": "朱猛", "ds_serv_phone": "18794210717", "ds_serv_mail": "zhumeng@lzb.ac.cn", "doi_value": "10.12072/ncdc.qlsst.db2693.2023", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-01-15 03:55:03", "last_updated": "2025-04-14 12:08:01", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.db2693.2023", "i18n": { "en": { "title": "High-resolution soil organic carbon density (0-100cm) data of Qilian Shan National Park", "ds_format": "GeoTiff", "ds_source": "This dataset is clipped from the Northwest China soil organic carbon density dataset and a total of 1,490 sampling points of actual soil measurements in the Northwest China were used to produce the product, with the following main sources of data:
\nProjects undertaken by this research team：
\nThe National Natural Science Foundation of China (NSFC) project \"Research on the variation pattern and mechanism of soil organic carbon along the gradient in Qilian Mountain\" (41771252), \"Investigation of Ecohydrological Sample Strips in the Heihe River Basin\" (91025002)、\"Soil Carbon Simulation in Typical Ecosystems of Arid Inland River Basins\" (31270482); Major Project in Gansu Province: \"Research on the Interaction between Ecosystems and Hydrological Processes in Qilian Mountains and Their Adaptation to Climate Change\" (18JR4RA002), The Science and Technology Innovation Project of Gansu Provincial Forestry and Grassland Bureau, \"Research on Ecological Management Effectiveness Assessment of Qilian Mountain National Park (Gansu Area)\" (GYCX[2020]01), Gansu Provincial Science and Technology Plan Funding Project \"Gansu Qilian Mountain Ecological Environment Research Centre\" ( 18JR2RA026) and other project data.
\nRaw Soil Profile Data, Literature Integration Data published by National Cryosphere Desert Data Centre and National Tibetan Plateau Data Center (Xu et al., 2018 DOI:10.1038/s41598-018-20764-9), WoSIS Soil Profile Database published by International Soil Reference and Information (ISRIC, https:// www.isric.org/explore/wosis), among others.
\nThe original data were quality controlled by removing sampling points with less than 0.001 latitude and longitude positioning accuracy, and abnormal values where the soil organic carbon content deviated significantly from the environmental background.
", "ds_quality": "The RMSE and R2 of the model were 6.26 kg/m2 and 0.75, respectively, after 30 times of 10-fold cross-validation. The final data product was divided into 33 sub-regions (regions), with mean and sd expressing the mean and standard deviation of 30 iterations of the model using units as kg/m2, representing the mass of soil organic carbon per unit area in the 0-100 cm soil layer. The mean and standard deviation (sd) of soil organic carbon density for the whole Qilian Shan region were obtained after combining the sub-regions, with a resolution of 10m and a data volume of 72.40GB. The Qilian Shan National Park boundary was masked and resampled to 30m/90m/250m/500m/1000m using the bilinear method, with a total data volume of 13.70GB.
", "ds_ref_way": "", "ds_abstract": "High-resolution soil organic carbon density (0-100cm) data of Qilian Shan National Park
", "ds_time_res": "年代", "ds_acq_place": "Qilian Mountain National Park", "ds_space_res": "10m/30m/90m/250m/500m/1000m", "ds_projection": "none", "ds_process_way": "Based on the 'Scorpan' (Soils, Climate, Organisms, Relief, Parent material, Age, Geographic position) framework, the spatial distribution of soil organic carbon density at 30m resolution in the 0-100 cm soil layer was simulated using the Digital Soil Mapping (DSM) method and a tile structure-based algorithm.
\nThe forecasting method is mainly based on the Extreme Gradient Boosting algorithm in machine learning, with environmental covariate data including: Landsat8 OLI multispectral imagery, Sentinel-1 radar imagery, raster data such as temperature, precipitation, radiation, topography, vegetation indices, and location.
\nThe digital soil mapping framework is constructed in R. In order to reduce memory usage and increase computing speed, the entire Qilian Mountains region is divided into 33 sub-regions (regions) of 150km×150km, and a mapping process based on tiles (tile size: 15km×15km) and parallel computing is constructed within each sub-region. The modelling is repeated by the bootstrap method for each. The spatial modelling of bootstrap samples is carried out to obtain the frequency distribution of the modelling results, and the modelling uncertainty is expressed as standard deviation (sd).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "祁连山国家公园", "土壤有机碳密度" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "祁连山国家公园" ], "ds_time_tags": [ 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "朱猛", "email": "zhumeng@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": "刘蔚", "email": "weiliu@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": "张举涛", "email": "jutzhang@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": "薛媛媛", "email": "xueyuanyuan@nieer.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": "张成琦", "email": "chengqizhang@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": "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": "朱猛", "email": "zhumeng@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": "张成琦", "email": "chengqizhang@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": "生态" }