中国陆域1∶100万植被指数UNVI多维数据集(2017)

UNVI multidimensional dataset of 2017 China’s terrestrial at 1∶1000000 scale

中国陆域1∶100万植被指数UNVI(Universal Normalized Vegetation Index)多维数据集(2017)是在分析MODIS地表反射率产品数据MOD09GA特点,参照传统的植被指数合成算法,为了满足植被长时间序列变化研究需要而生成的16 d合成UNVI数据集。本研究合成的UNVI数据集在反映植被密度、植被覆盖度、植被光合作用速率,以及反演植被理化参数方面,与传统的NDVI和EVI合成数据集相比,具有明显的优势。合成算法主要分为两步:首先对16 d合成周期内存在无效值和反射率负值的MODIS数据进行筛选处理;然后读取合成周期内的质量控制波段数据统计“无云”数据的天数,并根据“无云”数据的天数选择相应合成算法进行UNVI的16 d合成,从而获得2017年中国陆域时间分辨率为16 d,空间分辨率约为0.00286°的UNVI时间序列影像。基于本文提出的合成算法生成的中国陆域UNVI数据集,采用1∶100万标准经纬线分幅,共64景(Tile),每景所覆盖的经纬度范围为6°×4°,为方便起见,数据集采用MDD多维数据格式(Multi-Dimensional Dataset)存储,每个.mdd文件下存放每景2017年所有时相的影像数据。同时为便于数据下载,全部数据按照分幅压缩为64个.zip文件,压缩后的数据量约为3.78 GB。本数据集能为从事全球变化研究的科研人员提供更方便的植被指数长时间序列数据产品。

In this study,a Universal Normalized Vegetation Index(UNVI)composite optimization algorithm was designed on the basis of the data characteristics of MOD09GA and traditional vegetation index composite algorithm.This algorithm was used to generate the UNVI multidimensional dataset of the 1∶100000016-day composite vegetation index of China’s terrestrial in 2017.It provides convenient vegetation index long-term sequence data products for researchers engaged in global change research.The UNVI composite algorithm mainly consists two steps.The Moderate resolution imaging spectroradiometer data with invalid and negative reflectance values in the 16-day composite cycle are filtered.The number of days of the quality-free band data in the composite cycle is then counted.The corresponding synthesis algorithm for the 16-day composite of UNVI in accordance with the number of days of“cloudless”data is selected.The UNVI time series images with a time resolution of 16 days and a spatial resolution of approximately 0.00286°in China are generated using the proposed algorithm.The UNVI dataset adopts 1∶1000000 standard latitude and longitude framing,where the range covered by each scene is 6°×4°.For convenience,the dataset,including a total of 641∶10000 UNVI framing products in the country’s land area,is in multidimensional data format and stored in TSB mode.Researchers can select the corresponding regional vegetation index product download according to their research area.The UNVI dataset used in this study has obvious advantages compared with traditional normalized difference vegetation index and enhanced vegetation index composite datasets in reflecting vegetation density,vegetation coverage,vegetation photosynthesis rate,and inversion of vegetation physical and chemical parameters.Relevant researchers can use this dataset to conduct annual analysis of vegetation phenological changes.In addition,this dataset can be used to generate quantitative physicochemical parameter inversion products based on UNVI datasets and conduct research on phenological changes throughout the year.