本数据采用2023年哨兵2号光学卫星数据为数据源，按照区域及顺序，将全疆分为不同区域和不同类型图层。然后选择合适的深度学习模型依次对每一类要素制图，最后合并每种地类形成地表覆被。数据为CGCS2000坐标系，阿伯斯投影，精度为10米，共包括沙漠、森林、草地、耕地、水体、冰川和其他等7个土地覆被类别。
", "ds_source": "本数据采用2023年哨兵2号光学卫星Level-1C级数据为数据源。哨兵2（Sentinel-2）隶属于欧洲空间局（ESA）的哨兵计划（CopernicusProgramme）提供地球观测数据，主要用于获取高分辨率的光学遥感数据，可以用于多种应用，如土地监测、农业、林业、城市规划等。Level-1C数据包含卫星通过其传感器收集到的是经过几何精校正的正射影像，精度良好，满足使用需求。
", "ds_process_way": "本数据采用2023年哨兵2号光学卫星Level-1C级数据为数据源。哨兵2（Sentinel-2）隶属于欧洲空间局（ESA）的哨兵计划（CopernicusProgramme）提供地球观测数据，主要用于获取高分辨率的光学遥感数据，可以用于多种应用，如土地监测、农业、林业、城市规划等。Level-1C数据包含卫星通过其传感器收集到的是经过几何精校正的正射影像，精度良好，满足使用需求。
", "ds_quality": "数据空间分辨率高，分类的空间细节丰富，类别清晰完整。不同要素的整体精度OA均在0.9以上，其中耕地、水体、冰川整体精度最高，草地次之，数据符合技术要求。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": 97.53778, "ds_acq_lat_south": 34.00778, "ds_acq_lon_west": 72.22528, "ds_acq_lat_north": 48.915833, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1882138847, "ds_files_count": 12, "ds_format": ".data格式", "ds_space_res": "10米", "ds_time_res": "", "ds_coordinate": "CGCS2000", "ds_projection": "Albers投影", "ds_thumbnail": "4218fd0a-a0ed-49e0-b561-693d684cc90f.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据来源引用：新疆10m地表覆被数据集（2023年）来源于第三次新疆综合科学考察专项 \"空天地网一体化综合科考监测体系建设(2021xjkk1400)\"", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-12-04 18:17:43", "first_publish_time": null, "last_updated": "2026-01-21 10:25:08", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.XIEG.XJSEDATA.2024.00100167", "license": null, "extra": null, "files_shape": [ { "name": "2021xjkk1400-18-2023121618", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 10m Land Cover Data Set (2023)", "ds_abstract": "This data uses the 2023 Sentinel 2 optical satellite data as the data source, and divides the entire Xinjiang into different areas and different types of layer layers according to regions and order. Then select the appropriate deep learning model to map each type of element in turn, and finally merge each land type to form the surface cover. The data is in the CGCS2000 coordinate system, Abers projection, with an accuracy of 10 meters, and includes 7 land cover categories including desert, forest, grassland, cultivated land, water body, glacier and others.
", "ds_source": "This data uses 2023 Sentinel 2 optical satellite Level-1C data as the data source. Sentinel-2 is part of the European Space Agency (ESA)'s Copernicus Programme, which provides Earth observation data, which is mainly used to obtain high-resolution optical remote sensing data and can be used for various applications, such as land monitoring, agriculture, forestry, urban planning, etc. Level-1C data includes geometrically fine corrected orthophoto images collected by the satellite through its sensors, with good accuracy and meeting the needs of use.
", "ds_process_way": "This data uses 2023 Sentinel 2 optical satellite Level-1C data as the data source. Sentinel-2 is part of the European Space Agency (ESA)'s Copernicus Programme, which provides Earth observation data, which is mainly used to obtain high-resolution optical remote sensing data and can be used for various applications, such as land monitoring, agriculture, forestry, urban planning, etc. Level-1C data includes geometrically fine corrected orthophoto images collected by the satellite through its sensors, with good accuracy and meeting the needs of use.
", "ds_quality": "The data spatial resolution is high, the classification is rich in spatial details, and the categories are clear and complete. The overall accuracy OA of different elements is above 0.9, among which cultivated land, water bodies and glaciers have the highest overall accuracy, followed by grassland. The data meets the technical requirements.
", "ds_ref_instruction": "Data source citation: Xinjiang's 10-meter surface cover dataset (2023) comes from the third Xinjiang comprehensive scientific expedition special project \"Construction of Integrated Comprehensive Scientific Research Monitoring System of Air, Space, Space and Network (2021 xjkk1400)\"", "ds_format": ".data format", "ds_projection": "Albers projection", "ds_space_res": "10 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": [ "地表覆被" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ "李均力" ], "ds_meta_authors": [ "李均力" ], "ds_managers": [ "李均力", "李锦" ], "category": "土地利用/土地覆被" }