{ "created": "2022-03-26 17:56:05", "updated": "2026-05-01 07:59:30", "id": "c32f65d1-cb53-4505-b5d0-aa04bd264781", "version": 13, "ds_topic": null, "title_cn": "泾河流域土地利用数据LC10米数据集（2020年）", "title_en": "Land use data lc10m data set of Jinghe River Basin (2020)", "ds_abstract": "

  该数据集具有高覆盖性、时效性好（2020年）、分辨率高（10米）的特点，以泾河流域为研究区域，提取基于机器学习方法对哨兵2遥感影像处理的土地利用数据，分为九大类（水体、林地、草地、淹没植被、耕地、灌木、建筑、裸地、云层）。", "ds_source": "

  数据来源于https://livingatlas.arcgis.com/landcover/。", "ds_process_way": "

  基于10米分辨率的欧空局哨兵2号遥感影像。通过机器学习的方法获取2020年全年9类土地利用数据；\n

\n

  以泾河流域为研究区域，掩膜提取泾河流域2020年空间分辨率为10米的土地利用数据。", "ds_quality": "

  数据质量良好。", "ds_acq_start_time": "2020-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "泾河流域", "ds_acq_lon_east": 108.86583333333333, "ds_acq_lat_south": 34.65, "ds_acq_lon_west": 106.19166666666668, "ds_acq_lat_north": 37.39888888888889, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 27667654, "ds_files_count": 2, "ds_format": "栅格", "ds_space_res": "10米", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "UTM", "ds_thumbnail": "c32f65d1-cb53-4505-b5d0-aa04bd264781.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "bf1ae243-c102-4b2d-a2f6-b698468f4401", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.JRiver.db1973.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-04-08 08:44:06", "last_updated": "2025-06-30 15:52:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.JRiver.db1973.2022", "i18n": { "en": { "title": "Land use data lc10m data set of Jinghe River Basin (2020)", "ds_format": "raster data", "ds_source": "

    The data comes from https://livingatlas.arcgis.com/landcover/ .", "ds_quality": "

  Good data quality.", "ds_ref_way": "", "ds_abstract": "

  The data set has the characteristics of high coverage, good timeliness (2020) and high resolution (10m). Taking Jinghe River Basin as the research area, the land use data processed by sentry 2 remote sensing image based on machine learning method is extracted, which is divided into nine categories (water body, forest land, grassland, submerged vegetation, cultivated land, shrub, building, bare land and cloud).

", "ds_time_res": "年", "ds_acq_place": "Jinghe River Basin", "ds_space_res": "10米", "ds_projection": "UTM", "ds_process_way": "

  Based on the remote sensing image of ESA sentry 2 with a resolution of 10 meters. Obtain 9 types of land use data in 2020 through machine learning;\n

\n

  Taking the Jinghe River Basin as the research area, the land use data of Jinghe River basin with a spatial resolution of 10 meters in 2020 are extracted by mask.", "ds_ref_instruction": "When using the data, users should clearly state the source of the data in the text and quote the reference method provided by this metadata in the References section." } }, "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": [ "土地利用", "泾河流域", "遥感" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泾河流域" ], "ds_time_tags": [ 2020 ], "ds_contributors": [ { "true_name": "张耀南", "email": "yaonan@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": "李红星", "email": "lihongxing@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": "敏玉芳", "email": "myf@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" }