本数据是基于GLC_FCS10全球数据，叠加2024年新疆维吾尔自治区行政边界掩膜提取的新疆范围的数据集，土地利用类型分为耕地、森林、灌丛、草原、苔原、湿地、不透水表面、裸地、水域、永久冰雪10大类30小类，数据格式为栅格（.tif格式），数据空间分辨率为10米，数据坐标系为GCS_WGS_1984，数据年份为2023年。\n\n本次提供两种形式的数据，一种是按照原始数据以经纬度5°×5°瓦片的形式储存,没有经过掩膜处理（存放在文件夹GLC_FCS10maps_2023_5x5_XJ)，另一种是按边界掩膜处理后以5°×5°瓦片的形式储存（存放在文件夹GLC_FCS10maps_2023_5x5_XJ_Clip)。同时提供图层配色文件（GLC_FCS10maps_colormap.lyr)、数据论文全文和GLC_FCS10用户手册。
", "ds_source": "全球数据是中国科学院空天信息创新研究院研究员刘良云团队发布在zenodo平台上的数据。数据集简称为GLC_FCS10.该数据集融合Sentinel-1和Sentinel-2时间序列影像，借助Google Earth Engine平台构建，全球总体精度达83.16%，在美国精度高达85.09%，显著优于多款公开发布的10米级产品。与主流的10/30米产品比较，GLC_FCS10在分类丰富性、空间细节表达能力和分类精度方面均显示出明显优势。
", "ds_process_way": "GLC_FCS10数据集的生产采用了一套层级化、多源融合的精细加工框架：首先，基于多源先验产品（如GISD30不透水面、GWL_FCS30D湿地等）通过时空一致性分析和空间滤波生成高置信度训练样本，采用光谱质心法将30m样本降尺度至10m分辨率，并按层级策略分配样本（不透水面等量分配/自然地类按面积比例分配）；其次，融合Sentinel-2光学影像的时序百分位特征（含NDVI等指数）和纹理特征、Sentinel-1雷达数据的极化特征，以及ASTER GDEM地形参数构建多源特征集；接着采用三层级分类流程——先区分不透水面与自然地标（随机森林模型结合形态学优化），再分离湿地（滨海与内陆独立建模），最后精细化分类剩余20类自然地物，并通过983个5°×5°网格的局部自适应建模（纳入邻域3×3网格样本）解决空间异质性；最终经全局验证（56,121采样点，OA=83.16%）和第三方验证（美国LCMAP数据集，OA=85.09%）确认其精度超越主流产品，尤其在湿地识别（F1=66.63%）和耕地分类上表现突出。
", "ds_quality": "GLC_FCS10 总体精度达到 83.16%，在全球范围内 kappa 系数为 0.789，在美国总体精度为 85.09%。与五个已发布的 10 米或 30 米土地覆盖产品相比，也表明 GLC_FCS10 具有更高的精度。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "中国", "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": "login-access", "ds_total_size": 4905243677, "ds_files_count": 147, "ds_format": ".tif格式", "ds_space_res": "10米", "ds_time_res": "无", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "bc420ef1-3b1a-4dfe-8f2a-fa0cc114e85b.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "Liangyun, L., & Xiao, Z. (2025). GLC_FCS10: global 10 m land-cover dataset with fine classification system from Sentinel-1 and 2 time-series data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14729665", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-07-29 11:23:40", "first_publish_time": null, "last_updated": "2025-08-06 09:56:39", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "license": null, "extra": null, "files_shape": [ { "name": "GLC_FCS10 User Guides.pdf", "size": 313609, "is_dir": false }, { "name": "GLC_FCS10maps_colormap.lyr", "size": 11776, "is_dir": false }, { "name": "essd-2025-73.pdf", "size": 3229843, "is_dir": false }, { "name": "GLC_FCS10maps_2023_5x5_XJ", "size": null, "is_dir": true }, { "name": "GLC_FCS10maps_2023_5x5_XJ_Clip", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 10-meter finely classified land cover grid data in 2023 (GLC_FCS10)", "ds_abstract": "This data is based on GLC_FCS10 global data and superimposed on the Xinjiang data extracted from the administrative boundary mask of the Xinjiang Uygur Autonomous Region in 2024. The land use types are divided into 10 categories and 30 categories: cultivated land, forest, shrub, grassland, tundra, wetland, impermeable surface, bare land, water, and permanent ice and snow. The data format is grid (.tif format), and the data spatial resolution is 10 meters. The data coordinate system is GCS_WGS_1984, and the data year is 2023.\n\nTwo forms of data are provided this time. One is stored in the form of 5°×5° tiles with latitude and longitude according to the original data without masking (stored in the folder GLC_FCS10maps_2023_5x5_XJ), and the other is stored in the form of 5°×5° tiles after masking (stored in the folder GLC_FCS10maps_2023_5x5_XJ_Clip). The layer color file (GLC_FCS10maps_colormap.lyr), the full text of the data paper and the GLC_FCS10 user manual are also provided.
", "ds_source": "The global data is data released on the zenodo platform by Liu Liangyun, a researcher at the Institute of Aerospace Information Innovation of China Academy of Sciences. The dataset is referred to as GLC_FCS10 for short. The dataset integrates Sentinel-1 and Sentinel-2 time series images and is built with the help of the Google Earth Engine platform. The overall accuracy reaches 83.16% globally and 85.09% in the United States, which is significantly superior to many publicly released 10-meter products. Compared with mainstream 10/30-meter products, GLC_FCS10 shows obvious advantages in terms of classification richness, spatial detail expression capabilities and classification accuracy.
", "ds_process_way": "The production of the GLC_FCS10 dataset adopts a hierarchical, multi-source fusion fine processing framework: First, based on multi-source prior products (Such as GISD30 impervious surface, GWL_FCS30D wetland, etc.) High-confidence training samples are generated through spatio-temporal consistency analysis and spatial filtering, and the spectral mass center method is used to down-scale the 30m sample to 10m resolution, and the samples are assigned according to hierarchical strategies (The impermeable surface is equally distributed/the natural land type is distributed in proportion to the area); Secondly, the time-series percentile characteristics of Sentinel-2 optical images are integrated (including NDVI and other indices) and texture features, polarization characteristics of Sentinel-1 radar data, and ASTER GDEM terrain parameters to build a multi-source feature set; Then a three-level classification process is used-first distinguishing impervious surfaces from natural landmarks (Random forest model combined with morphological optimization), re-isolating wetlands (Independent modeling of coastal and inland areas), finally refined classification of the remaining 20 types of natural features and local adaptive modeling through 983 5°×5° grids (Including neighborhood 3×3 grid samples) to solve spatial heterogeneity; finally, global verification (56,121 sampling points, OA=83.16%) and third-party verification (U.S. LCMAP dataset, OA=85.09%) confirmed that its accuracy exceeds mainstream products, especially in wetland identification (F1=66.63%) and cultivated land classification.
", "ds_quality": "The overall accuracy of GLC_FCS10 reaches 83.16%, the kappa coefficient is 0.789 globally, and the overall accuracy in the United States is 85.09%. Compared with five released 10-meter or 30-meter land cover products, it also shows that GLC_FCS10 has higher accuracy.
", "ds_acq_place": "China", "ds_ref_instruction": "Liangyun, L., & Xiao, Z. (2025). GLC_FCS10: global 10 m land-cover dataset with fine classification system from Sentinel-1 and 2 time-series data [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14729665", "ds_format": ".tif format", "ds_projection": "WGS84", "ds_space_res": "10 meters", "ds_time_res": "no" } }, "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": [ "土地利用" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2023 ], "ds_contributors": [ "刘良云", "张晓" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "李锦" ], "category": "土地利用/土地覆被" }