本数据集的土壤实测数据主要来源于国家自然科学基金委项目（41771252、31270482、91025002）获取的坡面和流域尺度上263个采样点土壤有机碳密度数据。
\n\n根据“Scorpan”（Soils, Climate, Organisms, Relief, Parent material, Age, Geographic position）框架，基于数字土壤制图（Digital Soil Mapping, DSM）方法和瓦片结构运算，融合大野口流域2米DEM数据（张彦丽，2020）和Quickbird2.5m分辨率多光谱影像等数据（郭建文，2019），模拟了祁连山大野口流域0~100 cm土层2m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），利用气候、降水、辐射、地形、植被指数、光谱信息、空间位置等栅格数据作为输入变量进行空间制图。通过bootstrap的重复建模，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。经30次10折交叉验证表明，模型的RMSE和R2分别为5.34 kg/m2和 0.84。最终数据产品中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，表示单位面积上0~100cm土层内土壤有机碳的质量。
", "ds_source": "", "ds_process_way": "", "ds_quality": "良好
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": 100.30694444444444, "ds_acq_lat_south": 38.4375, "ds_acq_lon_west": 100.21583333333334, "ds_acq_lat_north": 38.57638888888889, "ds_acq_alt_low": 2590.0, "ds_acq_alt_high": 4645.0, "ds_share_type": "login-access", "ds_total_size": 254851097, "ds_files_count": 4, "ds_format": "GeoTiff", "ds_space_res": "2m", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "2d975743-d6aa-4465-98cb-337286eea328.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": "0931-4967575", "ds_serv_mail": "zhumeng@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-03-01 11:28:21", "last_updated": "2025-06-30 16:26:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.2021.22", "i18n": { "en": { "title": "2m resolution soil organic carbon density raster data in the Dayekou watershed of Qilian Mountains", "ds_format": "GeoTiff", "ds_source": "", "ds_quality": "Good
", "ds_ref_way": "", "ds_abstract": "The soil measured data in this dataset mainly comes from the soil organic carbon density data of 263 sampling points at the slope and watershed scales obtained by the National Natural Science Foundation of China projects (41771252, 31270482, 91025002).
\nBased on the \"Scorpion\" (Soil, Climate, Organisms, Relief, Parent material, Age, Geographic position) framework, digital soil mapping (DSM) method and tile structure calculation were used to simulate the spatial distribution of soil organic carbon density at a resolution of 2m in the 0-100 cm soil layer of the Dayekou watershed in the Qilian Mountains. This was achieved by integrating 2-meter DEM data from the Dayekou watershed (Zhang Yanli, 2020) and Quickbird 2.5m resolution multispectral images (Guo Jianwen, 2019). The prediction method is mainly based on the Extreme Gradient Boosting algorithm (XGBoost) in machine learning, which uses grid data such as climate, precipitation, radiation, terrain, vegetation index, spectral information, and spatial position as input variables for spatial mapping. Through repeated modeling of bootstrap, spatial modeling is performed on each bootstrap sample to obtain the frequency distribution of the modeling results. The uncertainty of the modeling is represented by standard deviation (SD). After 30 rounds of 10 fold cross validation, the RMSE and R2 of the model were 5.34 kg/m2 and 0.84, respectively. In the final data product, mean and SD represent the mean and standard deviation of 30 repeated modeling, respectively, in kg/m2, indicating the mass of soil organic carbon in the 0-100cm soil layer per unit area.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "2m", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [], "ds_time_tags": [], "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": "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" }, { "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": "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" } ], "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": "基础地理" }