黑河流域1km/5天合成植被覆盖度（FVC）数据集提供了2011-2014年的5天FVC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。将全国划分为不同植被区划、地类，分别计算植被指数（NDVI）与FVC的转换系数，采用计算的转换系数查找表和1km/5天合成NDVI产品生产区域1km/5天合成FVC产品。
\n黑河流域1km/5天合成FVC产品通过高分辨率数据可以直接获得植被覆盖比例，减轻低分辨率数据异质性的影响；另外，选择植被生长变化的典型时期，通过对每一个像元时间序列植被指数进行拟合得到每个像元对应的生长曲线参数；再配合土地利用图和植被分类图，寻找区域的代表性均一像元用于训练植被指数的转换系数。
\n通过与黑河流域高分辨率ASTER参考FVC结果相比，首先联合地面实测数据，利用尺度上推方法，将黑河流域ASTER产品聚合到 1km 尺度得到ASTER聚合FVC数据，并与Geoland2项目发布的基于SPOT VEGETATION遥感数据的FVC产品（简称GEOV1 FCOVER）进行间接比较，根据三种数据FVC时间序列曲线图，结果表明：GEOV1的结果较ASTER 影像联合地面实测的结果偏高，黑河流域1km/5天合成FVC产品结果位于两者之间，在实验区内黑河流域1km/5天合成FVC产品优于GEOV1产品。
\n总之，黑河流域1km/5天合成FVC数据集综合利用多源遥感数据以提高FVC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1km/5天合成植被覆盖度（FVC）数据集提供了2011-2014年的5天FVC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": "
将全国划分为不同植被区划、地类，分别计算植被指数（NDVI）与FVC的转换系数，采用计算的转换系数查找表和1km/5天合成NDVI产品生产区域1km/5天合成FVC产品。黑河流域1km/5天合成FVC产品通过高分辨率数据可以直接获得植被覆盖比例，减轻低分辨率数据异质性的影响；另外，选择植被生长变化的典型时期，通过对每一个像元时间序列植被指数进行拟合得到每个像元对应的生长曲线参数；再配合土地利用图和植被分类图，寻找区域的代表性均一像元用于训练植被指数的转换系数。
", "ds_quality": "数据质量良好", "ds_acq_start_time": "2011-01-12 00:00:00", "ds_acq_end_time": "2014-01-11 00:00:00", "ds_acq_place": "黑河流域, 中游人工绿洲试验区, 上游寒区水文试验区, 下游天然绿洲试验区", "ds_acq_lon_east": 101.96000000000001, "ds_acq_lat_south": 37.74, "ds_acq_lon_west": 97.11, "ds_acq_lat_north": 42.69, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 35372480, "ds_files_count": 6, "ds_format": "hdr", "ds_space_res": "/", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "/", "ds_thumbnail": "21e5c74c-4455-42ef-b3d6-d8aa681cf70b.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 09:57:18", "last_updated": "2025-06-30 16:34:41", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.31", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing experiment: 1km / 5-day synthetic vegetation coverage (FVC) data set of Heihe River Basin (2011-2014)", "ds_format": "hdr", "ds_source": "
The 1km / 5-day synthetic vegetation coverage (FVC) data set in Heihe River basin provides the 5-day FVC synthesis results from 2011 to 2014. The data uses Terra / MODIS, Aqua / MODIS, and 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 1km / 5-day synthetic vegetation coverage (FVC) data set in Heihe River basin provides the 5-day FVC synthesis results from 2011 to 2014. The data uses Terra / MODIS, Aqua / MODIS, and 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. The whole country is divided into different vegetation divisions and land types, and the conversion coefficient between vegetation index (NDVI) and FVC is calculated respectively. The calculated conversion coefficient lookup table and 1km / 5-day synthetic NDVI product production area are used to synthesize FVC products
\nSynthetic FVC products of 1km / 5 days in Heihe River basin can directly obtain the vegetation coverage ratio through high-resolution data, so as to reduce the impact of heterogeneity of low-resolution data; In addition, the typical period of vegetation growth and change is selected, and the growth curve parameters corresponding to each pixel are obtained by fitting the vegetation index of each pixel time series; Then, combined with the land use map and vegetation classification map, find the representative uniform pixels of the region for training the conversion coefficient of vegetation index
\nCompared with the high-resolution aster reference FVC results of Heihe River Basin, firstly, combined with the ground measured data, the aster products of Heihe River Basin are aggregated to 1km scale by using the scale up method, and the aster aggregated FVC data are obtained, which is indirectly compared with the FVC products based on spot vector remote sensing data released by geoland2 project (geov1 fcover for short), According to the FVC time series curves of three kinds of data, the results show that the results of geov1 are higher than those of ASTER image combined with ground measurement, the results of 1km / 5-day synthetic FVC products in Heihe River Basin are in between, and the 1km / 5-day synthetic FVC products in Heihe River Basin are better than geov1 products in the experimental area
\nIn short, the 1km / 5-day synthetic FVC data set in Heihe River basin makes comprehensive use of multi-source remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products and better serve the application of remote sensing data products.
", "ds_time_res": "年", "ds_acq_place": "Heihe River Basin, artificial oasis test area in the middle reaches, hydrological test area in the upper cold area, and natural oasis test area in the lower reaches", "ds_space_res": "/", "ds_projection": "/", "ds_process_way": "The whole country is divided into different vegetation divisions and land types, and the conversion coefficient between vegetation index (NDVI) and FVC is calculated respectively. The calculated conversion coefficient lookup table and 1km / 5-day synthetic NDVI product production area are used to synthesize FVC products. Synthetic FVC products of 1km / 5 days in Heihe River basin can directly obtain the vegetation coverage ratio through high-resolution data, so as to reduce the impact of heterogeneity of low-resolution data; In addition, the typical period of vegetation growth and change is selected, and the growth curve parameters corresponding to each pixel are obtained by fitting the vegetation index of each pixel time series; Then, combined with the land use map and vegetation classification map, find the representative uniform pixels of the region for training the conversion coefficient of vegetation index
", "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": [ "植被覆盖度", "遥感产品", "生态遥感产品", "LUCC数据" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河流域", "中游人工绿洲试验区", "上游寒区水文试验区", "下游天然绿洲试验区" ], "ds_time_tags": [ 2011, 2012, 2013, 2014 ], "ds_contributors": [ { "true_name": "穆西晗", "email": "muxihan@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院遥感科学国家重点实验室", "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": "柳钦火", "email": "qhliu@irsa.ac.cn", "work_for": "中国科学院遥感应用研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "穆西晗", "email": "muxihan@bnu.edu.cn", "work_for": "北京师范大学地理学与遥感科学学院遥感科学国家重点实验室", "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": "遥感及产品" }