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0~100 cm土层30m分辨率土壤有机碳密度空间分布。
", "ds_source": "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、41901100、91025002、31270482），甘肃省重大项目（18JR4RA002）等项目集成的432个土壤剖面数据。
", "ds_process_way": "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、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。
", "ds_quality": "经30次10折交叉验证表明，模型的RMSE和R2分别为5.58 kg/m2和 0.79。最终数据产品共分为142个瓦片（Tile），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，文件名中的经纬度表示该瓦片的中心位置。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": 700.0, "ds_acq_alt_high": 5500.0, "ds_share_type": "login-access", "ds_total_size": 8526530694, "ds_files_count": 286, "ds_format": "TIFF", "ds_space_res": "30", "ds_time_res": "", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "88685b1f-e4b4-482c-b953-2e496fd3c98e.jpg", "ds_thumb_from": 2, "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": "2021-02-24 14:22:13", "last_updated": "2025-06-30 16:26:13", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.2021.21", "i18n": { "en": { "title": "30m grid data of soil organic carbon density from 0-100cm in Qilian Mountains", "ds_format": "TIFF", "ds_source": "The soil measurement data in this dataset mainly comes from 432 soil profile data integrated by projects such as the National Natural Science Foundation of China (41771252, 41901100, 91025002, 31270482) and the Gansu Provincial Major Project (18JR4RA002).
", "ds_quality": "After 30 rounds of 10 fold cross validation, the RMSE and R2 of the model were found to be 5.58 kg/m2 and 0.79, respectively. The final data product is divided into 142 tiles, where 'mean' and 'sd' represent the mean and standard deviation of 30 repeated models, respectively. The unit is kg/m2, which represents the mass of soil organic carbon in the 0-100cm soil layer per unit area. The latitude and longitude in the file name indicate the center position of the tile.
", "ds_ref_way": "", "ds_abstract": "Based on the \"Scorpion\" (Soil, Climate, Organisms, Relief, Parent material, Age, Geographic position) framework, the spatial distribution of soil organic carbon density at a resolution of 30m in the 0-100 cm soil layer of the Qilian Mountains was simulated using the Digital Soil Mapping (DSM) method and operations based on tile structures.
", "ds_time_res": "", "ds_acq_place": "", "ds_space_res": "30", "ds_projection": "", "ds_process_way": "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, 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).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 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, "ds_topic_tags": [ "祁连山；土壤碳；碳密度；1米" ], "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": "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" } ], "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" }