黑河流域2015年1km/5天合成叶面积指数（LAI）数据集提供了2015年的5天LAI合成结果，该数据利用Terra/MODIS、Aqua/MODIS、以及国产卫星FY3A/MERSI和FY3B/MERSI传感器数据构建空间分辨率1km、时间分辨率5天的多源遥感数据集。\n
多源遥感数据集可在有限时间内提供比单一传感器更多的角度和更多次的观测，但是，由于传感器的在轨运行时间及性能差异，多源数据集的观测质量参差不齐。因此，为更有效的利用多源数据集，算法首先对多源数据集进行了质量分级，根据观测合理性分为一级数据、二级数据、三级数据。\n
三级数据为受薄云污染的观测，不用于计算。质量评估及分级的目的是为LAI反演时最优数据集的选择及反演算法流程设计提供依据。叶面积指数产品反演算法设计为区分山地平地、区分植被类型使用不同模型的神经网络法反演。\n
基于全球DEM图和地表分类图，针对草地和农作物等连续植被采用PROSAIL模型，针对森林和山地植被采用坡面GOST模型。利用黑河上游森林和中游绿洲的地面实测数据生成的参考图，并将对应的高分辨率参考图升尺度到1km分辨率，与LAI产品进行比较，产品在农田和森林区域与参考值间均具有良好的相关性，总体精度基本满足GCOS规定的误差不超过 (0.5, 20%)的精度阈值。将本产品与MODIS、GEOV1和GLASS等LAI产品进行交叉对比，相比较参考值而言，本LAI产品精度优于同类产品。\n
总之，黑河流域1km/5天合成LAI数据集综合利用多源遥感数据以提高LAI参数产品的估算精度、时间分辨率等，更好的服务于遥感数据产品的应用。
", "ds_source": "黑河流域2015年1km/5天合成叶面积指数（LAI）数据集提供了2015年的5天LAI合成结果，该数据利用Terra/MODIS、Aqua/MODIS、以及国产卫星FY3A/MERSI和FY3B/MERSI传感器数据构建空间分辨率1km、时间分辨率5天的多源遥感数据集。
", "ds_process_way": "叶面积指数产品反演算法设计为区分山地平地、区分植被类型使用不同模型的神经网络法反演。\n
基于全球DEM图和地表分类图，针对草地和农作物等连续植被采用PROSAIL模型，针对森林和山地植被采用坡面GOST模型。利用黑河上游森林和中游绿洲的地面实测数据生成的参考图，并将对应的高分辨率参考图升尺度到1km分辨率，与LAI产品进行比较，产品在农田和森林区域与参考值间均具有良好的相关性，总体精度基本满足GCOS规定的误差不超过 (0.5, 20%)的精度阈值
", "ds_quality": "数据质量良好
", "ds_acq_start_time": "2015-01-01 00:00:00", "ds_acq_end_time": "2016-01-01 00:00:00", "ds_acq_place": "黑河流域", "ds_acq_lon_east": 101.96194444444444, "ds_acq_lat_south": 37.74, "ds_acq_lon_west": 97.11222222222221, "ds_acq_lat_north": 42.69, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 225765370, "ds_files_count": 4, "ds_format": "tif", "ds_space_res": null, "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "5abf078f-5fc0-498d-9cdc-4fe9af31943b.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "本数据由“黑河生态水文遥感试验（HiWATER）”产生，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": "c94b3578-20da-4346-9de9-c702b6ca8983", "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": "2021-09-14 09:56:33", "last_updated": "2025-06-30 16:35:37", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.37", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing test: 1km / 5-day synthetic leaf area index (LAI) data set of Heihe River Basin (2015)", "ds_format": "TIFF", "ds_source": "The 2015 1km / 5-day synthetic leaf area index (LAI) data set of Heihe River basin provides the 5-day Lai synthesis results in 2015. The data uses Terra / MODIS, Aqua / MODIS, domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with spatial resolution of 1km and temporal resolution of 5 days
", "ds_quality": "Good data quality
", "ds_ref_way": "", "ds_abstract": "The 2015 1km / 5-day synthetic leaf area index (LAI) data set of Heihe River basin provides the 5-day Lai synthesis results in 2015. The data uses Terra / MODIS, Aqua / MODIS, domestic satellites fy3a / MERSI and fy3b / MERSI sensor data to build a multi-source remote sensing data set with spatial resolution of 1km and temporal resolution of 5 days
\nMulti-source remote sensing data sets can provide more angles and more observations than a single sensor in a limited time. However, the observation quality of multi-source data sets is uneven due to the difference of on orbit running time and performance of sensors. Therefore, in order to make more effective use of multi-source data sets, the algorithm first classifies the quality of multi-source data sets, which are divided into primary data, secondary data and tertiary data according to the observation rationality.\n
\nLevel III data are observations polluted by thin clouds and are not used for calculation. The purpose of quality evaluation and classification is to provide basis for the selection of optimal data set and the design of inversion algorithm flow in Lai inversion. The inversion algorithm of leaf area index product is designed to distinguish mountain and flat land and vegetation types, and the neural network method of different models is used for inversion.\n
\nBased on global DEM map and surface classification map, PROSAIL model is adopted for continuous vegetation such as grassland and crops, and slope gost model is adopted for forest and mountain vegetation. The reference map generated by using the ground measured data of forests in the upper reaches of Heihe River and oases in the middle reaches of Heihe River, and the corresponding high-resolution reference map is scaled up to 1km resolution. Compared with Lai products, the products have good correlation between farmland and forest areas and the reference value, and the overall accuracy basically meets the accuracy threshold that the error specified by GCOS does not exceed (0.5, 20%). This product is cross compared with Lai products such as MODIS, geov1 and glass. Compared with the reference value, the accuracy of this Lai product is better than that of similar products.\n
\nIn short, the 1km / 5-day synthetic Lai data set in Heihe River basin makes comprehensive use of multi-source remote sensing data to improve the estimation accuracy and time resolution of Lai parameter products and better serve the application of remote sensing data products
", "ds_time_res": "日", "ds_acq_place": "Heihe River Basin", "ds_space_res": "", "ds_projection": "", "ds_process_way": "The inversion algorithm of leaf area index product is designed to distinguish mountain and flat land and vegetation types, and the neural network method of different models is used for inversion
\nBased on global DEM map and surface classification map, PROSAIL model is adopted for continuous vegetation such as grassland and crops, and slope gost model is adopted for forest and mountain vegetation. The reference map generated by using the ground measured data of forests in the upper reaches of Heihe River and oases in the middle reaches of Heihe River, and the corresponding high-resolution reference map is scaled up to 1km resolution. Compared with Lai products, the products have good correlation between farmland and forest areas and the reference value, and the overall accuracy basically meets the accuracy threshold specified by GCOS that the error does not exceed (0.5, 20%)
", "ds_ref_instruction": "This data is generated by \"Heihe eco hydrological remote sensing experiment (hiwater)\". 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 reference part." } }, "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": [ "叶面积指数，土地利用，多源遥感数据集" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河流域" ], "ds_time_tags": [ 2015 ], "ds_contributors": [ { "true_name": "赵静", "email": "", "work_for": "北京师范大学", "country": "中国" }, { "true_name": "仲波", "email": "zhongbo@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所遥感科学国家重点实验室", "country": "中国" }, { "true_name": "吴俊君", "email": "", "work_for": "中国科学院空天信息创新研究院遥感科学国家重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "赵静", "email": "", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "赵静", "email": "", "work_for": "北京师范大学", "country": "中国" } ], "category": "遥感及产品" }