{
    "created": "2026-04-13 19:47:23",
    "updated": "2026-04-14 10:15:51",
    "id": "cfd5c932-b482-44e7-b5cf-a357d664cd38",
    "version": 4,
    "ds_topic": null,
    "title_cn": "中国西北干旱地区5类碳密度数据集（2013-2023年）",
    "title_en": "Five types of carbon density datasets in arid northwest China (2013-2023)",
    "ds_abstract": "<p>&emsp;&emsp;本数据集以中国西北干旱区为研究对象，整合了2013-2023年发表文献数据，并结合研究团队获取的实测样本点数据，系统汇总了地上生物量碳密度（AGBC）、地下生物量碳密度（BGBC）、土壤有机碳密度（SOC）、土壤无机碳密度（SIC）和死有机碳密度（DOC）等多类型碳密度样本信息。同时，数据集融合了土地利用、气候因子和土壤属性等多源数据。基于 Random Forest、XGBoost 和 SVR 等机器学习模型，对区域碳密度空间分布进行了模拟与制图，并验证了XGBoost 模型在预测精度方面的优势。该数据集空间分辨率为250 m，单位为kg C m⁻²，可为西北干旱区碳储量评估、碳循环研究及生态保护与管理决策提供科学的数据支撑。",
    "ds_source": "<p>&emsp;&emsp;本数据源于2013-2023年发表文献数据，并结合研究团队获取的实测样本点数据。",
    "ds_process_way": "<p>&emsp;&emsp;基于 Random Forest、XGBoost 和 SVR 等机器学习模型，对区域碳密度空间分布进行了模拟与制图，并验证了XGBoost 模型在预测精度方面的优势。",
    "ds_quality": "<p>&emsp;&emsp;数据质量较好。",
    "ds_acq_start_time": "2013-01-01 00:00:00",
    "ds_acq_end_time": "2023-12-31 00:00:00",
    "ds_acq_place": "中国西北",
    "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": "open-access",
    "ds_total_size": 608927256,
    "ds_files_count": 0,
    "ds_format": "",
    "ds_space_res": "250m",
    "ds_time_res": "",
    "ds_coordinate": "WGS84",
    "ds_projection": "",
    "ds_thumbnail": "cfd5c932-b482-44e7-b5cf-a357d664cd38.png",
    "ds_thumb_from": 2,
    "ds_ref_way": "",
    "paper_ref_way": "",
    "ds_ref_instruction": "None",
    "ds_from_station": null,
    "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a",
    "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": "2026-04-14 10:39:30",
    "last_updated": "2026-04-14 10:39:30",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.NCDC.ECOLOGY.DB7301.2026",
    "i18n": {
        "en": {
            "title_en": "Five types of carbon density datasets in arid northwest China (2013-2023)",
            "ds_format_en": "",
            "ds_source_en": "",
            "ds_quality_en": "",
            "ds_ref_way_en": "",
            "ds_abstract_en": "",
            "ds_time_res_en": "",
            "ds_acq_place_en": "Northwest China",
            "ds_space_res_en": "",
            "ds_projection_en": "",
            "ds_process_way_en": "",
            "ds_ref_instruction_en": ""
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "CC BY 4.0",
    "ds_topic_tags": [
        "碳密度",
        "中国西北",
        "干旱地区"
    ],
    "ds_subject_tags": [
        "自然地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "西北干旱区"
    ],
    "ds_time_tags": [
        2013,
        2014,
        2015,
        2016,
        2017,
        2018,
        2019,
        2020,
        2021,
        2022,
        2023
    ],
    "ds_contributors": [
        {
            "true_name": "张宏伟",
            "email": "11230867@stu.lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        },
        {
            "true_name": "别强",
            "email": "bieq@lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "张宏伟",
            "email": "11230867@stu.lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        },
        {
            "true_name": "别强",
            "email": "bieq@lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "张宏伟",
            "email": "11230867@stu.lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        },
        {
            "true_name": "别强",
            "email": "bieq@lzjtu.edu.cn",
            "work_for": "兰州交通大学",
            "country": "中国"
        }
    ],
    "category": "生态"
}