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，以渭河流域为研究区域，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，分为十大类（水体、树木、草地、被淹没的植被、农作物、灌丛/灌木、建筑区、裸地、雪/冰、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10类土地利用数据。\n
\n以渭河流域为研究区域，掩膜提取渭河流域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": "渭河流域", "ds_acq_lon_east": 110.27444444444444, "ds_acq_lat_south": 33.69611111111111, "ds_acq_lon_west": 103.9713888888889, "ds_acq_lat_north": 37.40833333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 86522560, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "10", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "UTM", "ds_thumbnail": "eb0782b6-074c-4787-b703-de95effe6557.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0d5fea4e-6fd7-4c70-b28b-3f91204c579a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-09-28 17:38:04", "last_updated": "2025-05-29 11:35:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.WRiver.2021.12", "i18n": { "en": { "title": "Land use LC 10-meter dataset for the Weihe River Basin (2020)", "ds_format": "tif", "ds_source": "
Data from https://livingatlas.arcgis.com/landcover/.", "ds_quality": "
Data quality is good.", "ds_ref_way": "", "ds_abstract": "
The dataset is characterized by high coverage, good timeliness (2020), and high resolution (10 m). Taking the Weihe River Basin as the study area, the land use data processed by Sentinel 2 remote sensing imagery based on the machine learning method were extracted and classified into ten major categories (water bodies, trees, grasslands, submerged vegetation, crops, thickets/shrubs, built-up areas, bare ground, snow/ice, and cloud cover).
", "ds_time_res": "年", "ds_acq_place": "Weihe River Basin", "ds_space_res": "10", "ds_projection": "UTM", "ds_process_way": "Based on ESA Sentinel 2 remote sensing imagery at 10-meter resolution. Ten categories of land-use data for the whole year of 2020 were acquired by machine learning methods.\n\n
Taking the Weihe River Basin as the study area, the mask extracted land use data with a spatial resolution of 10 meters in 2020 for the Weihe River Basin.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "ds_topic_tags": [ "土地利用", "渭河流域", "遥感" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "渭河流域" ], "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" }