中国陆域植被指数UNVI多维数据产品(2018-2021)

China's Terrestrial UNVI Multidimensional Dataset(2018-2021)

中国陆域植被指数UNVI(Universal Normalized Vegetation Index)多维数据集产品通过MODIS地表反射率产品MOD09GA,基于通用模式分解算法UPDM(Universal Pattern Decomposition Method)以16天为合成周期计算而成,相较于传统NDVI产品,UNVI在反映植被覆盖变化和植被理化参量的定量反演方面更有优势。合成算法以合成周期内无云数据天数N为判断条件,以角度归一化合成法、有限视角内最大值合成法)、直接计算法以及最大值合成法MVC作为主要合成算法进行UNVI的计算,从而完成时间分辨率为16天、空间分辨率约为463m的2018-2021年中国陆域植被指数UNVI产品的合成。UNVI数据的存储格式为MDD多维数据格式(Multi-Dimensional Dataset),该数据集包含2018-2021年以16天为间隔的23个时相的中国陆域范围植被指数UNVI产品。

Vegetation index data products are widely used in the inversion of physical and chemical parameters of vegetation,land cover classification and change studies.Traditional vegetation index products,such as AVHRR-NDVI,are sensitive to soil background changes and have the problem of high value saturation,which easily causes a decrease in sensitivity to vegetation detection.China's terrestrial UNVl multidimensional dataset(2018-2021)was developed based on the MODIS surface reflectance product MODo9GA using the Universal Pattern Decomposition algorithm UPDM(Universal Pattern Decomposition Method),which takes 16 days as the synthesis cycle.The practices show that the UNVI has more advantages in reflecting the change in vegetation cover and quantitative inversion of vegetation physical and chemical parameters compared with traditional NDVI products.The synthesis algorithm takes the number of days without cloud data in the synthesis period N as the judgment condition and uses the angle normalized synthesis method,the maximum synthesis method in a limited perspective,the direct calculation method and the maximum synthesis method MvC as the main synthesis algorithm to calculate the UNVI.Thus,the 2018-2021 China terrestrial UNVI products with a time resolution of 16 d and a spatial resolution of approximately 463 m were synthesized.The dataset includes the UNVI products of China terrestrial vegetation indices in 23 time periods with 16 d intervals from 2018 to 2021.UNVI vegetation index products can provide more comprehensive and convenient long-time series vegetation index data products for scholars participating in research on global change and human activities.