{
    "created": "2025-08-12 13:23:39",
    "updated": "2026-08-08 05:47:42",
    "id": "5a547df8-0f83-450b-a42c-a6b2b30eceb8",
    "version": 6,
    "ds_topic": null,
    "title_cn": "基于全球土壤信息数据（SoilGrids2.0）的新疆土壤特性数据集",
    "title_en": "",
    "ds_abstract": "<p>本数据是来自全球土壤信息中心公开发布的全球土壤特性图SoilGrids v2.0，更多信息请参考官网信息https://www.isric.org/explore/soilgrids</p>",
    "ds_source": "<p>SoilGrids是一个基于全球土壤剖面数据和环境层汇编的数字土壤制图系统，该系统采用最先进的机器学习方法来绘制全球土壤特性的空间分布。SoilGrids 预测模型使用 WoSIS 数据库中的超过 230,000 个土壤剖面观测值以及一系列环境协变量进行拟合。协变量从超过 400 个地球观测衍生产品和其他环境信息（包括气候、土地覆盖和地形形态）的库中选取。\nSoilGrids全球土壤特性图按照全球土壤制图国际土壤科学联合会及其规范规定的六个标准深度间隔（0-5、5-15、15-30、30-60、60-100、100-200厘米），以 250 米的空间分辨率制作。土壤特性包括pH值、容重、沙土含量、淤泥含量、粉质土含量、阳离子交换容量、总氮、土壤有机碳含量、土壤有机碳密度和土壤有机碳储量等11个指标。\n预测不确定性通过 90%预测区间的下限和上限来量化。在 soilgrids.org 上显示的额外不确定性层是四分位距与中位数的比值。\nSoilGrids 地图在 CC-BY 4.0 许可证下公开发布。\n此外，根据世界土壤资源参比基础（WRB）和美国农业部土壤分类系统（USCS）预测了基岩深度和土壤类别分布。2022 年进行了重要更新，增加了新的土壤属性数据，并改进了数据处理算法。采用GCS_WGS_1984坐标系</p>\n<p>数据官网https://soilgrids.org/</p>",
    "ds_process_way": "",
    "ds_quality": "",
    "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": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "login-access",
    "ds_total_size": 3051996816,
    "ds_files_count": 70,
    "ds_format": "GeoTiff格式",
    "ds_space_res": "250米",
    "ds_time_res": "",
    "ds_coordinate": "WGS84",
    "ds_projection": "GCS_WGS84",
    "ds_thumbnail": "5a547df8-0f83-450b-a42c-a6b2b30eceb8.png",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6",
    "doi_value": "",
    "subject_codes": [
        "170.45",
        "210.5010",
        "210.5015"
    ],
    "quality_level": 1,
    "publish_time": "2025-08-13 19:03:00",
    "first_publish_time": null,
    "last_updated": "2025-08-13 19:03:00",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": null,
    "license": null,
    "extra": null,
    "files_shape": [
        {
            "name": "指标中英文对照.xlsx",
            "size": 15285,
            "is_dir": false
        },
        {
            "name": "Xinjiang",
            "size": null,
            "is_dir": true
        }
    ],
    "features": null,
    "data_level": 0,
    "i18n": {
        "en": {
            "title": "Xinjiang soil characteristics dataset based on Global Soil Information Data (SoilGrids2.0)",
            "ds_abstract": "<p>This data is from the Global Soil Characteristics Map SoilGrids v2.0 publicly released by the Global Soil Information Center. For more information, please refer to the official website information https://www.isric.org/explore/soilgrids</p>",
            "ds_source": "<p>SoilGridds is a digital soil mapping system based on global soil profile data and environmental layer compilation. The system uses state-of-the-art machine learning methods to map the spatial distribution of global soil characteristics. The SoilGrids prediction model uses more than 230,000 soil profile observations from the WoSIS database and a range of environmental covariates to fit it. Covariates were selected from more than 400 libraries of Earth observation derivatives and other environmental information, including climate, land cover, and topography.\nThe SoilGrids global soil characteristics map is produced with a spatial resolution of 250 meters according to six standard depth intervals (0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm) specified by the International Union of Soil Sciences for Global Soil Mapping and its specifications. Soil characteristics include 11 indicators including pH value, bulk density, sand content, silt content, silty soil content, cation exchange capacity, total nitrogen, soil organic carbon content, soil organic carbon density and soil organic carbon storage.\nPrediction uncertainty is quantified by the lower and upper limits of the 90% forecast interval. The additional layer of uncertainty shown on soilgrids.org is the ratio of the interquartile range to the median.\nSoilGridds maps are publicly released under the CC-BY 4.0 license.\nIn addition, bedrock depth and soil class distribution were predicted based on the World Soil Resources Reference Base (WRB) and the U.S. Department of Agriculture Soil Classification System (USCS). Important updates were made in 2022, adding new soil attribute data and improving data processing algorithms. Adopt GCS_WGS_1984 coordinate system</p>\n<p>Data official website soilgrids.org/</p>",
            "ds_format": "GeoTiff format",
            "ds_projection": "GCS_WGS84",
            "ds_space_res": "250 meters"
        }
    },
    "license_type": null,
    "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": [
        "土壤特性"
    ],
    "ds_subject_tags": [
        "地理学",
        "土壤物理学",
        "土壤化学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "新疆"
    ],
    "ds_time_tags": [],
    "ds_contributors": [
        "李锦"
    ],
    "ds_meta_authors": [
        "李锦"
    ],
    "ds_managers": [
        "李锦"
    ],
    "category": "土壤"
}