西北地区90m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1490个采样点土壤实测数据，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。\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法重采样至90m，数据量8.10GB。
", "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": null, "ds_share_type": "open-access", "ds_total_size": 8708464545, "ds_files_count": 10, "ds_format": "GeoTiff", "ds_space_res": "90", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "无", "ds_thumbnail": "2064ef7f-1cb8-4f05-9ca3-3395f52617dd.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": "", "subject_codes": [], "quality_level": 3, "publish_time": "2023-01-15 01:42:41", "last_updated": "2025-04-14 12:44:15", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.qlsst.db2690.2023", "i18n": { "en": { "title": "90m resolution soil organic carbon density (0-100cm) data in northwest China", "ds_format": "GeoTiff", "ds_source": " <p>The production of this data set uses 1490 sampling points of soil measured data, and the main sources of data are as follows:</p>\n(1) The research team undertook the projects of the National Natural Science Foundation of China, \"Research on the law and mechanism of soil organic carbon change along the gradient in Qilian Mountains\" (41771252), \"Investigation of ecological and hydrological transects in the Heihe River Basin\" (91025002), and \"Research on soil carbon simulation of typical ecosystems in the arid inland river basin\" (31270482), The data of the major project of Gansu Province, \"Research on the interaction between Qilian Mountain water conservation ecosystem and hydrological process and its adaptation to climate change\" (18JR4RA002), the scientific and technological innovation project of Gansu Provincial Forestry and Grass Administration, \"Research on the effectiveness of ecological governance in Qilian Mountain National Park (Gansu Area)\" (GYCX [2020] 01), and the project funded by Gansu Provincial Science and Technology Plan, \"Gansu Qilian Mountain Ecological Environment Research Center\" (18JR2RA026).\n(2) The original soil profile data, document integration data (Xu et al., 2018 DOI: 10.1038/s41598-018-20764-9), and the World Soil Reference and Information Center (ISRIC) WoSIS soil profile database published by the National Glacier Frozen Desert Scientific Data Center and the Qinghai-Tibet Plateau Scientific Data Center（ https://www.isric.org/explore/wosis ）Etc. After the quality control of the original data, the sampling points with the accuracy of longitude and latitude positioning less than 0.001 and the abnormal values of soil organic carbon content significantly deviating from the environmental background were removed\n
", "ds_quality": " <p>After 30 times of 10 fold cross validation, the RMSE and R2 of the model are 6.15 kg/m2 and 0.74 respectively. The final data product is divided into 21 sub-regions, where mean and sd respectively represent the mean and standard deviation of 30 repeated modeling, with the unit of kg/m2, representing the quality of soil organic carbon per unit area in the 0~100cm soil layer. The combined mean and standard deviation of soil organic carbon density in the whole northwest region are obtained after the combination of each sub-region, with the data volume of 63.70GB. The bilinear method is used to resample to 90m, with the data volume of 8.10GB<</p>\n <p>90m resolution soil organic carbon density (0-100cm) data in northwest China<</p>\n <p>According to the framework of \"Scorpan\" (Soils, Climate, Organisms, Relief, Parent material, Age, Geographic position), the spatial distribution of soil organic carbon density with a resolution of 30 m in the 0-100 cm soil layer in the northwest region was simulated using the digital soil mapping (DSM) method and the calculation based on tile structure. The prediction method is mainly based on the extreme gradient boosting algorithm (XGBoost, eXtreme Gradient Boosting) in machine learning. The environmental covariate data include: Landsat8 OLI multispectral image, Sentinel-1 radar image, temperature, precipitation, radiation, terrain, vegetation index, location and other grid data.\nThe digital soil mapping framework is built in R language. In order to reduce the memory consumption and improve the calculation speed, the whole northwest region is divided into 21 600km × 500km sub-region, each sub-region is built based on tiles (tile size: 45km × 45km) and parallel computing. The bootstrap method is used to repeat the modeling, and each bootstrap sample is spatially modeled to obtain the frequency distribution of the modeling results. The uncertainty of modeling is expressed by standard deviation (sd)\n
", "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, "is_paper_in_submitting": false, "ds_topic_tags": [ "西北地区", "土壤有机碳密度", "30m" ], "ds_subject_tags": [], "ds_class_tags": [], "ds_locus_tags": [ "西北地区" ], "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": "基础地理" }