本数据为利用sentinel2号反射率产品数据进行进一步加工，其中原始数据为全疆10米月度哨兵二号反射率镶嵌数据集，叶面积指数通过LAI-2000仪器实地进行采集，质量控制主要采用与实地采集数据进行叶面积指数的反演后的高精度植被覆盖信息进行精度评价。本数据集在高叶面积指数区域需要进行进一步验证。
", "ds_source": "哨兵二号反射率数据主要来源为欧空局下载。其余辅助数据来源为实地测量数据，自主产生。
", "ds_process_way": "本数据集通过机器学习支持向量机进行叶面积指数的计算。数据分辨率为10m，用于本模型计算的叶面积指数数据通过实地LAI-2000仪器测算，进行LAI的高精度监督分类提取。
", "ds_quality": "通过由吐鲁番哈密地区，艾比湖流域，南疆塔里木河流域北缘的实测测量的叶面积指数进行对比后，总精度在85-90%，本数据集在绿洲和荒漠区域精度较好，但山区森林区域精度还未做进一步评价。
", "ds_acq_start_time": null, "ds_acq_end_time": null, "ds_acq_place": "", "ds_acq_lon_east": 97.42528, "ds_acq_lat_south": 32.889168, "ds_acq_lon_west": 72.23028, "ds_acq_lat_north": 49.608612, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1241753048325, "ds_files_count": 20, "ds_format": "栅格", "ds_space_res": "10米", "ds_time_res": "月", "ds_coordinate": "WGS84", "ds_projection": "CGCS2000", "ds_thumbnail": "04eaa39a-c21f-49ab-986f-e55f8b6e0357.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据来源引用：新疆10m叶面积指数数据集（2024年4-10月）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 18:58:23", "first_publish_time": null, "last_updated": "2026-01-21 10:26:35", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.01337", "license": null, "extra": null, "files_shape": [ { "name": "2021xjkk1400-118-2024120501", "size": null, "is_dir": true } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Xinjiang 10m leaf area index dataset (April to October 2024)", "ds_abstract": "This data is for further processing using sentinel2 reflectance product data. The original data is the 10-meter monthly Sentinel 2 reflectance mosaic data set in Xinjiang. The leaf area index is collected on the spot through the LAI-2000 instrument. The quality control mainly uses high-precision vegetation coverage information after inversion of the leaf area index with field collected data for accuracy evaluation. This dataset needs further verification in areas with high leaf area index.
", "ds_source": "The main source of Sentinel-2 reflectivity data is downloaded from ESA. The rest of the auxiliary data sources are on-site measurement data and are generated independently.
", "ds_process_way": "This dataset uses machine learning support vector machines to calculate the leaf area index. The data resolution is 10m. The leaf area index data used for calculation of this model is calculated by on-site LAI-2000 instrument to carry out high-precision supervised classification and extraction of LAI.
", "ds_quality": "After comparing the leaf area index measured in Turpan's Hami City, Aibi Lake Basin, and the northern edge of the Tarim River Basin in southern Xinjiang, the overall accuracy is 85-90%. The accuracy of this dataset is good in oases and desert areas, but the accuracy of mountainous forest areas has not been further evaluated.
", "ds_ref_instruction": "Data source citation: Xinjiang's 10m leaf area index dataset (April to October 2024) 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": "grid", "ds_projection": "CGCS2000", "ds_space_res": "10 meters", "ds_time_res": "months" } }, "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": [ "叶面积指数" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "新疆" ], "ds_time_tags": [ 2024 ], "ds_contributors": [ "刘铁", "马勇刚" ], "ds_meta_authors": [ "马勇刚" ], "ds_managers": [ "李锦" ], "category": "哨兵" }