黑河流域1km/5day植被指数（NDVI/EVI）数据集提供了2011-2014年的5天分辨率NDVI/EVI合成产品，该数据利用我国国产卫星FY-3数据兼具较高时间分辨率（1天）和空间分辨率（1km）的特点构造多角度观测数据集，在对多源数据集以及现有合成植被指数产品及算法进行分析的基础上，提出了基于多源数据集生产1km分辨率5天周期的全球合成植被指数产品算法体系。\n
植被指数合成算法基本采用MODIS的植被指数合成算法，即基于半经验的Walthall模型的BRDF角度归一化方法、CV-MVC法和MVC法的算法体系。利用该算法体系，分别对一级数据、二级数据计算合成植被指数，并进行质量标识。多源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，算法体系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。三级数据为受薄云污染的观测，不用于计算。\n
在黑河中游农田、森林区域的验证结果表明，联合多时相、多角度观测数据的NDVI/EVI合成结果与地面实测数据具有较好的一致性（RMSE=0.105）。与MODIS MOD13A2产品的时间序列对比分析，充分显示了时间分辨率从16天提高到5天时，稳定的高精度的植被指数对植被生长细节的细致描述。\n
总之，黑河流域1km/5day合成植被指数（NDVI/EVI）数据集综合利用多时相、多角度观测数据以提高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/5day植被指数（NDVI/EVI）数据集提供了2011-2014年的5天分辨率NDVI/EVI合成产品，该数据利用我国国产卫星FY-3数据兼具较高时间分辨率（1天）和空间分辨率（1km）的特点构造多角度观测数据集，在对多源数据集以及现有合成植被指数产品及算法进行分析的基础上，提出了基于多源数据集生产1km分辨率5天周期的全球合成植被指数产品算法体系。
", "ds_process_way": "植被指数合成算法基本采用MODIS的植被指数合成算法，即基于半经验的Walthall模型的BRDF角度归一化方法、CV-MVC法和MVC法的算法体系。利用该算法体系，分别对一级数据、二级数据计算合成植被指数，并进行质量标识。多源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，算法体系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。
", "ds_quality": "数据质量良好
", "ds_acq_start_time": "2011-01-01 00:00:00", "ds_acq_end_time": "2015-01-01 00:00:00", "ds_acq_place": "黑河流域", "ds_acq_lon_east": 101.75, "ds_acq_lat_south": 37.25, "ds_acq_lon_west": 97.75, "ds_acq_lat_north": 42.1, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 92968327, "ds_files_count": 11, "ds_format": "tif", "ds_space_res": "/", "ds_time_res": "日", "ds_coordinate": "WGS84", "ds_projection": "/", "ds_thumbnail": "fa7346f6-1beb-492a-b18a-bfe0ac55a42e.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 09:56:45", "last_updated": "2025-06-30 16:34:43", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.NIEER.2021.36", "i18n": { "en": { "title": "Heihe River eco hydrological remote sensing test: 1km / 5-day synthetic vegetation index (NDVI / EVI) data set of Heihe River Basin (2011-2014)", "ds_format": "TIFF", "ds_source": "The 1km / 5day vegetation index (NDVI / EVI) data set in Heihe River basin provides a 5-day resolution NDVI / EVI synthetic product from 2011 to 2014. The data uses the characteristics of domestic satellite FY-3 data with high temporal resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set, Based on the analysis of multi-source data sets and existing synthetic vegetation index products and algorithms, a global synthetic vegetation index product algorithm system with 1km resolution and 5-day cycle based on multi-source data sets is proposed
", "ds_quality": "Good data quality
", "ds_ref_way": "", "ds_abstract": "The 1km / 5day vegetation index (NDVI / EVI) data set in Heihe River basin provides a 5-day resolution NDVI / EVI synthetic product from 2011 to 2014. The data uses the characteristics of domestic satellite FY-3 data with high temporal resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set, Based on the analysis of multi-source data sets and existing synthetic vegetation index products and algorithms, a global synthetic vegetation index product algorithm system with 1km resolution and 5-day cycle based on multi-source data sets is proposed
\nThe vegetation index synthesis algorithm basically adopts the vegetation index synthesis algorithm of MODIS, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on semi empirical waltall model. Using the algorithm system, the synthetic vegetation index is calculated for the primary data and secondary data respectively, and the quality is identified. Multi-source 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 differences in on orbit running time and performance of sensors. Therefore, in order to make more effective use of multi-source data sets, the algorithm system 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. Level III data are observations polluted by thin clouds and are not used for calculation
\nThe verification results in farmland and forest areas in the middle reaches of Heihe River show that the NDVI / EVI synthesis results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 products, it fully shows that when the time resolution is increased from 16 days to 5 days, the stable and high-precision vegetation index can describe the details of vegetation growth in detail
\nIn short, the 1km / 5day synthetic vegetation index (NDVI / EVI) data set in Heihe River basin makes comprehensive use of multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of 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 vegetation index synthesis algorithm basically adopts the vegetation index synthesis algorithm of MODIS, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on semi empirical waltall model. Using the algorithm system, the synthetic vegetation index is calculated for the primary data and secondary data respectively, and the quality is identified. Multi-source 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 differences in on orbit running time and performance of sensors. Therefore, in order to make more effective use of multi-source data sets, the algorithm system 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
", "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" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黑河流域" ], "ds_time_tags": [ 2011, 2012, 2013, 2014 ], "ds_contributors": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "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" } ], "ds_meta_authors": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "李静", "email": "lijing01@radi.ac.cn", "work_for": "中国科学院遥感与数字地球研究所", "country": "中国" } ], "category": "遥感及产品" }