西北地区500m及以上分辨率土壤有机碳密度数据
", "ds_source": "本数据集的制作共使用1490个采样点土壤实测数据，数据主要来源如下： （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。 （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，模拟了西北地区0~100 cm土层30m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：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。 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，将整个西北地区划分为21个600km×500km的子区域（region），每个子区域内构建基于瓦片（瓦片大小：45km×45km）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6.15 kg/m2和 0.74。最终数据产品共分为21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西北地区合土壤有机碳密度均值（mean）和标准差（sd），数据量63.70GB，采用bilinear法分别重采样至500m、1000m、5km、10km，最终产品总数据量135MB。
", "ds_acq_start_time": "2010-06-01 00:00:00", "ds_acq_end_time": "2020-09-30 00:00:00", "ds_acq_place": "西北地区", "ds_acq_lon_east": 108.7, "ds_acq_lat_south": 31.6, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 49.2, "ds_acq_alt_low": -154.0, "ds_acq_alt_high": 8611.0, "ds_share_type": "open-access", "ds_total_size": 142037436, "ds_files_count": 2, "ds_format": "GeoTiff", "ds_space_res": "500m/1000m/5km/10km", "ds_time_res": "年代", "ds_coordinate": "WGS84", "ds_projection": "无", "ds_thumbnail": "fc626829-a7e6-4d0d-a407-246d2f5b37b2.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "(1)冯起, 常宗强, 席海洋, 等. 基于碳氮循环的中蒙荒漠生态脆弱区生态系统对全球变化响应研究. 地球科学进展, 2022, 37(11): 1101-1114.\n(2)冯起, 白光祖, 李宗省, 王宝, 陈丽娟, 王鹏龙, 鱼腾飞, 孟鸿飞, 刘文, 陆志翔, 宁婷婷, 张成琦, 朱猛. 加快构建西北地区生态保护新格局.中国科学院院刊, 2022, 37(10): 1457-1470.\n(3)Li Yongge, Liu Wei, Feng Qi, Zhu Meng, Yang Linshan, Zhang. Effects of land use and land cover change on soil organic carbon storage in the Hexi Regions, Northwest China. Journal of Environmental Management, 2022, 312, 114911.\nMeng Zhu, Qi Feng, Yanyan Qin, et al. 2019. The role of topography in shaping the spatial patterns of soil organic carbon. Catena, 176: 296-305.", "ds_from_station": null, "organization_id": "8fa8eec3-a891-44cd-b60e-f6af19a16bba", "ds_serv_man": "朱猛", "ds_serv_phone": "+8618794210717", "ds_serv_mail": "zhumeng@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-07-27 11:03:31", "last_updated": "2025-07-27 15:45:00", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.QLSST.DB6906.2025", "i18n": { "en": { "title": "Soil organic carbon density data with a resolution of 500m and above in the northwest region", "ds_format": "GeoTiff", "ds_source": "The production of this dataset used a total of 1490 soil measurement data from sampling points. The main sources of the data are as follows: (1) The research team undertook the National Natural Science Foundation of China project \"Study on the Gradient Variation Law and Mechanism of Soil Organic Carbon in Qilian Mountains\" (41771252), \"Investigation of Ecological Hydrological Sample Belt in Heihe River Basin\" (91025002), \"Simulation Study of Soil Carbon in Typical Ecosystems of Arid Inland River Basins\" (31270482), the Gansu Provincial Major Project \"Study on the Interaction between Qilian Mountain Water Conservation Ecosystem and Hydrological Processes and Their Adaptation to Climate Change\" (18JR4RA002), and the Gansu Provincial Forestry and Grassland Bureau Science and Technology Innovation Project \"Evaluation of Ecological Governance Effectiveness in Qilian Mountain National Park (Gansu Area)\" (GYCX002). [2020] 01) Data from projects funded by the Gansu Provincial Science and Technology Plan, such as the \"Gansu Qilian Mountain Ecological Environment Research Center\" (18JR2RA026). (2) The original soil profile data and literature integration data (Xu et al., 2018 DOI: 10.1038/s41598-018-20764-9) publicly released by the National Glacial Permafrost Desert Science Data Center and the Qinghai Tibet Plateau Science Data Center, as well as the World Soil Reference and Information Center (ISRIC) WoSIS soil profile database（ https://www.isric.org/explore/wosis ）Wait. The raw data has undergone quality control to remove sampling points with latitude and longitude positioning accuracy less than 0.001, as well as outliers with significant deviation between soil organic carbon content and environmental background.
", "ds_quality": "After 30 rounds of 10 fold cross validation, the RMSE and R2 of the model were 6.15 kg/m2 and 0.74, respectively. The final data product is divided into 21 sub regions, where mean and SD represent the mean and standard deviation of 30 repeated modeling, respectively, in kg/m2, indicating the mass of soil organic carbon per unit area in the 0-100cm soil layer. After merging each sub region, the mean and standard deviation of soil organic carbon density in the entire northwest region are obtained, with a data volume of 63.70GB. The bilinear method is used to resample to 500m, 1000m, 5km, and 10km, respectively, resulting in a total data volume of 135MB for the final product
", "ds_ref_way": "", "ds_abstract": "Soil organic carbon density data with a resolution of 500m and above in the northwest region
", "ds_time_res": "年代", "ds_acq_place": "Northwest China", "ds_space_res": "500m/1000m/5km/10km", "ds_projection": "None", "ds_process_way": "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 in the northwest region was simulated using the Digital Soil Mapping (DSM) method and operations based on tile structures. The prediction method is mainly based on the Extreme Gradient Boosting algorithm (XGBoost) in machine learning. Environmental covariate data includes Landsat 8 OLI multispectral images, Sentinel-1 radar images, temperature, precipitation, radiation, terrain, vegetation index, location and other raster data. Constructing a digital soil mapping framework in R language, in order to reduce memory usage and improve computing speed, the entire northwest region is divided into 21 sub regions of 600km × 500km. Within each sub region, a mapping process based on tiles (tile size: 45km × 45km) and parallel computing is constructed. The bootstrap method is used to repeat modeling, and spatial modeling is performed on each bootstrap sample to obtain the frequency distribution of the modeling results. The modeling uncertainty is represented by standard deviation (sd).
", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "西北地区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "西北地区", "土壤有机碳密度", "500m分辨率" ], "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": "基础地理" }