本数据集提供了中国北方干旱区草地土壤有机碳、无机碳、全碳及颗粒有机碳与矿物结合有机碳的含量信息，同时收录对应样点的气候、植被与土壤属性。数据覆盖2020年5-8月植被生长旺季，沿4000千米的干旱梯度共布设30个独立样点，经度92.48°-123.45°E，海拔134-2894m，在每个采样点50m×50m的样地内布设3个0.5m×0.5m重复样方，每个样方内用直径5cm土钻采集3根0-15cm表层土芯，最终共收集到180份土壤样品。碳组分采用湿筛法分离并用元素分析仪进行含量测定；辅助变量（气候、植物生物量、土壤属性）均来自公开数据集或论文。数据可为干旱区草地碳循环、碳库稳定性及气候敏感性研究提供基础支撑。", "ds_source": "
数据测定包括：①使用元素分析仪对SIC和STC的含量进行定量，SOC的含量由STC减SIC得出；②烘干法测定土壤水分含量；③根据粒径分组法，从SOC中分离出POC和MAOC。\n辅助数据收集环节根据每个采样点的坐标，我们从不同的数据集中提取了对应的气候、生物和土壤变量。干旱指数来自全球干旱指数数据库(https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/5)。生物变量包括植物AGBC和MBC，其中AGBC来自Spawn等人的数据集(https://www.nature.com/articles/s41597-020-0444-4)，MBC数据来自https://zenodo.org/records/6950624。土壤pH值和沙粒含量来自中国30×30弧秒分辨率的网格数据集(https://doi.org/10.11888/Soil.tpdc.270281)。", "ds_process_way": "
Mass recovery(%)=(Mass_POM+Mass_MAOM)/Mass_Bulk soil ×100; POC(mgCg^(-1) Bulk soil)=(Mass_POM×OC_POM)/(Mass_Bulk soil×Mass recovery/100)。Mass_Bulk soil是用于样品湿筛的土的质量(g)；Mass_POM和Mass_MAOM为湿筛后回收的POM和MAOM组分质量(g)；OC_POM是POM组分中测得的碳浓度（mg C g−1）。", "ds_quality": "
在每个采样点布设3个重复样方，每个样方内取3根土芯以确保重复。", "ds_acq_start_time": "2020-05-01 00:00:00", "ds_acq_end_time": "2020-08-31 00:00:00", "ds_acq_place": "中国北方干旱草地", "ds_acq_lon_east": 92.48, "ds_acq_lat_south": 36.9, "ds_acq_lon_west": 123.45, "ds_acq_lat_north": 45.76, "ds_acq_alt_low": 134.0, "ds_acq_alt_high": 2893.0, "ds_share_type": "login-access", "ds_total_size": 56538, "ds_files_count": 2, "ds_format": "Excel", "ds_space_res": "1000m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "a8c81046-4f40-4396-ae0f-7142d0443341.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "6d0aa454-9b64-4be5-b0cd-4cc796e6aea0", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-03-13 18:43:07", "last_updated": "2026-03-13 18:43:07", "protected": false, "protected_to": "2028-03-01 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.DESERTIFICATION.DB7170.2026", "i18n": { "en": { "title": "Dataset of Soil Carbon Components in Grasslands of Arid Regions in Northern China (2020)", "ds_format": "Excel", "ds_source": "
Data determination includes: ① Quantifying the content of SIC and STC using an elemental analyzer, and obtaining the content of SOC by subtracting SIC from STC; ② Drying method to determine soil moisture content; ③ Separate POC and MAOC from SOC using particle size grouping method.\nIn the auxiliary data collection stage, we extracted corresponding climate, biological, and soil variables from different datasets based on the coordinates of each sampling point. The drought index comes from the Global Drought Index Database（ https://figshare.com/articles/dataset/Global_Aridity_Index_and_Potential_Evapotranspiration_ET0_Climate_Database_v2/7504448/5 ）Biological variables include plant AGBC and MBC, with AGBC coming from the dataset of Spawn et al（ https://www.nature.com/articles/s41597-020-0444-4 ）MBC data comes from https://zenodo.org/records/6950624 The soil pH value and sand content are obtained from a grid dataset with a resolution of 30 × 30 arc seconds in China（ https://doi.org/10.11888/Soil.tpdc.270281 ）.", "ds_quality": "
Set up 3 replicate plots at each sampling point, and take 3 soil cores from each plot to ensure reproducibility.", "ds_ref_way": "", "ds_abstract": "
This dataset provides information on the content of organic carbon, inorganic carbon, total carbon, particulate organic carbon, and mineral bound organic carbon in grassland soils in the arid regions of northern China. It also includes the climate, vegetation, and soil properties of corresponding sampling points. The data covers the peak season of vegetation growth from May to August 2020. A total of 30 independent sampling points were set up along a 4000 kilometer drought gradient, with a longitude of 92.48 ° -123.45 ° E and an altitude of 134-2894m. Three 0.5m × 0.5m replicate plots were set up in each sampling point's 50m × 50m plot, and three 0-15cm surface soil cores were collected using a 5cm diameter soil drill in each plot. Finally, a total of 180 soil samples were collected. The carbon components were separated using a wet sieve method and their content was determined using an elemental analyzer; The auxiliary variables (climate, plant biomass, soil properties) are all derived from publicly available datasets or papers. The data can provide basic support for the study of carbon cycling, carbon pool stability, and climate sensitivity in arid grassland.", "ds_time_res": "", "ds_acq_place": "Arid grasslands in northern China", "ds_space_res": "1000m", "ds_projection": "", "ds_process_way": "
Mass recovery(%)=(Mass_POM+Mass_MAOM)/Mass_Bulk soil ×100; POC (mgCg ^ (-1) Bulk soil)=(Mass-POM × OC-POM)/(Mass-Bulk soil × Mass recovery/100). Mass_Sulk soil is the mass (g) of soil used for wet sieving of samples; Mass. POM and Mass. MAOM are the mass (g) of POM and MAOM components recovered after wet screening; OC-POM is the carbon concentration measured in the POM component (mg C g − 1).", "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": [ "草地生态系统", "干旱梯度", "干旱敏感性", "矿物结合有机碳" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国北方干旱草地" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "刘慧颖", "email": "hyliu@des.ecnu.edu.cn", "work_for": "华东师范大学", "country": "中国" }, { "true_name": "熊可歆", "email": "51263903060@stu.ecnu.edu.cn", "work_for": "华东师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "熊可歆", "email": "51263903060@stu.ecnu.edu.cn", "work_for": "华东师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "刘慧颖", "email": "hyliu@des.ecnu.edu.cn", "work_for": "华东师范大学", "country": "中国" } ], "category": "基础地理" }