{
    "created": "2023-02-22 10:44:00",
    "updated": "2026-04-22 20:55:04",
    "id": "f511f110-19ca-43d5-8ddf-2a2dca9773fe",
    "version": 5,
    "ds_topic": null,
    "title_cn": "祁连山国家公园表层20cm及100cm土壤有机碳密度分布数据集（2000-2100年）",
    "title_en": "Soil organic carbon density maps in the top 20 cm and 100 cm soil profile in the Qilian Mountain National Park during 2000 through 2100",
    "ds_abstract": "<p>&emsp;&emsp;该数据包含了2000-2015年祁连山国家公园表层20 cm及100 cm的1 km×1 km分辨率土壤有机碳密度逐年空间分布图和三种气候情景（RCP2.6/4.5/8.5）下2016-2100年表层20 cm及100 cm的1 km×1 km分辨率土壤有机碳密度逐年预测空间分布图。",
    "ds_source": "<p>&emsp;&emsp;利用基于地面实测样本构建的数据驱动模型对区域尺度土壤有机碳进行模拟构建。",
    "ds_process_way": "<p>&emsp;&emsp;利用Python语言对时空数据进行处理，并基于“scikit-learn”库构建土壤有机碳空间反演模型，对不同深度土壤有机碳空间分布进行模拟，未来气候数据利用delta空间降尺度方法校正和降尺度。",
    "ds_quality": "<p>&emsp;&emsp;1.原始数据来源于公开发表文献和数据库，其精度由原文献作者充分验证和控制；\n<p>&emsp;&emsp;2.模型构建过程完全按照数据驱动模型标准构建方案进行，方法规范，并且采用交叉验证方法对模型稳健性、鲁棒性进行了充分的验证；\n<p>&emsp;&emsp;3.采用了观测数据和主流土壤有机碳空间分布数据等多源数据对模拟结果的质量进行了充分对比验证，证明了本研究模拟结果的准确性和可靠性。",
    "ds_acq_start_time": "2000-01-01 00:00:00",
    "ds_acq_end_time": "2100-12-31 00:00:00",
    "ds_acq_place": "祁连山国家公园",
    "ds_acq_lon_east": 103.01666666666667,
    "ds_acq_lat_south": 36.755833333333335,
    "ds_acq_lon_west": 95.12083333333332,
    "ds_acq_lat_north": 39.74638888888889,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "apply-access",
    "ds_total_size": 140223363,
    "ds_files_count": 2,
    "ds_format": "NC4",
    "ds_space_res": "1km",
    "ds_time_res": "年",
    "ds_coordinate": "无",
    "ds_projection": "",
    "ds_thumbnail": "f511f110-19ca-43d5-8ddf-2a2dca9773fe.jpg",
    "ds_thumb_from": 2,
    "ds_ref_way": "吴一平，祁连山国家公园表层20cm及100cm土壤有机碳密度分布数据集（2000-2100年），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2023，doi：10.12072/ncdc.nieer.db2735.2023",
    "paper_ref_way": "",
    "ds_ref_instruction": "",
    "ds_from_station": null,
    "organization_id": "52109486-75ef-4764-a933-6380c6f42432",
    "ds_serv_man": "敏玉芳",
    "ds_serv_phone": "0931-4967596",
    "ds_serv_mail": "ncdc@lzb.ac.cn",
    "doi_value": "10.12072/ncdc.nieer.db2735.2023",
    "subject_codes": [
        "170.45"
    ],
    "quality_level": 3,
    "publish_time": "2023-02-28 15:54:09",
    "last_updated": "2023-03-01 11:31:04",
    "protected": false,
    "protected_to": "2024-01-01 00:00:00",
    "lang": "zh",
    "cstr": "11738.11.ncdc.nieer.db2735.2023",
    "i18n": {
        "en": {
            "title": "Soil organic carbon density maps in the top 20 cm and 100 cm soil profile in the Qilian Mountain National Park during 2000 through 2100",
            "ds_format": "",
            "ds_source": "<pre><code>\n</code></pre>\n<p>&emsp; A data-driven model based on ground measured samples was used to simulate soil organic carbon at regional scale.",
            "ds_quality": "<pre><code>\n</code></pre>\n<p>&emsp; 1. The original data is from published documents and databases, and its accuracy is fully verified and controlled by the author of the original document;\n<p>&emsp; 2. The model construction process is completely carried out in accordance with the standard construction scheme of data-driven model, and the method is standardized, and the robustness and robustness of the model are fully verified by the cross-validation method;\n<p>&emsp; 3. The quality of the simulation results was fully compared and verified by using multi-source data such as observation data and mainstream soil organic carbon spatial distribution data, which proved the accuracy and reliability of the simulation results of this study.",
            "ds_ref_way": "",
            "ds_abstract": "<pre><code>\n</code></pre>\n<p>  This data includes 1 km of 20 cm and 100 cm of the surface layer of Qilian Mountain National Park from 2000 to 2015 × Annual spatial distribution map of soil organic carbon density with 1 km resolution and 1 km of surface 20 cm and 100 cm in 2016-2100 under three climate scenarios (RCP2.6/4.5/8.5) × The spatial distribution map of soil organic carbon density with 1 km resolution is predicted year by year.</p>",
            "ds_time_res": "年",
            "ds_acq_place": "Qilian Mountain National Park",
            "ds_space_res": "1km",
            "ds_projection": "",
            "ds_process_way": "<pre><code>\n</code></pre>\n<p>&emsp; Use Python language to process spatiotemporal data, and build a soil organic carbon spatial inversion model based on the \"scikit-learn\" database to simulate the spatial distribution of soil organic carbon at different depths. The future climate data will be calibrated and scaled down using the delta spatial scaling method.",
            "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": [
        "土壤有机碳",
        "祁连山",
        "气候情景",
        "未来预测"
    ],
    "ds_subject_tags": [
        "地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "祁连山国家公园"
    ],
    "ds_time_tags": [
        2000,
        2100
    ],
    "ds_contributors": [
        {
            "true_name": "吴一平",
            "email": "rocky.ypwu@gmail.com",
            "work_for": "西安交通大学",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "李汇文",
            "email": "lihuiwen@stu.xjtu.edu.cn",
            "work_for": "西安交通大学",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "吴一平",
            "email": "rocky.ypwu@gmail.com",
            "work_for": "西安交通大学",
            "country": "中国"
        }
    ],
    "category": "其他"
}