河西三大内陆河流域（黑河、石羊河、疏勒河）高分辨率土壤有机碳密度（0~100cm）数据
", "ds_source": "本数据集裁剪自西北地区土壤有机碳密度数据集，产品制作共使用了西北地区1490个采样点土壤实测数据，数据主要来源如下：
\n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。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。\n
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0~100 cm土层30m分辨率土壤有机碳密度空间分布。
\n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：Landsat8 OLI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语言中构建基于瓦片和并行计算的制图框架，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6.15 kg/m2和 0.74。原始数据分辨率为30m，利用石羊河、黑河、疏勒河流域边界分别掩膜至目标区域，采用bilinear法分别重采样至90m、250m、500m、1000m，最终产品总数据量3.90GB。
", "ds_acq_start_time": "2010-06-01 00:00:00", "ds_acq_end_time": "2020-09-30 00:00:00", "ds_acq_place": "河西地区", "ds_acq_lon_east": 105.0, "ds_acq_lat_south": 43.0, "ds_acq_lon_west": 92.0, "ds_acq_lat_north": 37.0, "ds_acq_alt_low": 900.0, "ds_acq_alt_high": 5500.0, "ds_share_type": "open-access", "ds_total_size": 4196327392, "ds_files_count": 62, "ds_format": "GeoTiff", "ds_space_res": "30m/90m/250m/500m/1000m", "ds_time_res": "年代", "ds_coordinate": "WGS84", "ds_projection": "无", "ds_thumbnail": "bba38dfe-a6e5-4cc3-84fe-166c148dcdf4.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.db2698.2023", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-01-15 11:09:30", "last_updated": "2025-04-14 13:14:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.db2698.2023", "i18n": { "en": { "title": "High-resolution soil organic carbon density (0-100 cm) data in the three major inland river basins (Heihe River, Shiyang River and Shule River) of the hexi area", "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": "30 times 10-fold cross-validation showed that the RMSE and R2 of the model were 6.15 kg/m2 and 0.74 respectively. The original data resolution is 30m, and the Shiyang, Hehe and Shule river basin boundaries were masked to the target area using the bilinear method to resample to 90m, 250m, 500m and 1000m respectively, with a total final product data volume of 3.90GB.
", "ds_ref_way": "", "ds_abstract": "High-resolution soil organic carbon density (0-100 cm) data in the three major inland river basins (Heihe River, Shiyang River and Shule River) of the hexi area.
", "ds_time_res": "年代", "ds_acq_place": "Hexi area", "ds_space_res": "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.
\nA framework of tile-based and parallel computing mapping is constructed in R. The modelling is repeated by the bootstrap method, and the spatial modelling is performed for each bootstrap sample to obtain the frequency distribution of the modelling results, with the modelling uncertainty expressed as standard deviation (sd).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "土壤有机碳密度", "河西地区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "河西地区" ], "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" }