%0 Dataset
%T MODIS mod13a2 enhanced vegetation index (EVI) data set of Weihe River Basin (2000-2020)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/e1bcfbda-4e37-4e5c-b87e-14321daddfbd
%W NCDC
%R 10.12072/ncdc.WRiver.db0023.2021
%A Zhang yaonan
%K Enhanced vegetation index;vegetation cover;canopy structure;leaf area index
%X Enhanced vegetation index (EVI) is improved from normalized vegetation index (NDVI). Comprehensive atmospheric correction is carried out according to the image factors included in atmospheric correction, such as atmospheric molecules, aerosols, thin clouds, water vapor and ozone. Evi atmospheric correction is divided into three steps. The first step is cloud removal. The second step is atmospheric correction, which includes atmospheric molecules, aerosols, water vapor, etc. in addition to the existing Rayleigh scattering and ozone of NDVI. The third step is to further deal with the impact of residual aerosols by means of the difference between blue and red light passing through aerosols. Since the input NIR, red and blue are subject to relatively strict atmospheric correction, it is not necessary to use the vegetation index based on NIR / red ratio in order to eliminate multiplicative noise when designing the vegetation index formula. Therefore, the problem of easy saturation of vegetation index and lack of linear relationship with actual vegetation coverage caused by this is solved. Evi is more sensitive to the changes of canopy structure, including leaf area index (LAI), canopy type, vegetation phase and canopy structure. Based on MODIS mod13a2.005 enhanced vegetation index evi data set, the segmented images covering the Weihe River Basin are processed by batch splicing, projection conversion and clipping using MRT tools and python language code to generate MODIS mod13a2 evi data of the Weihe River Basin from 2000 to 2020. The spatial resolution of this data set is 1 km and the temporal resolution is 16 days.