%0 Dataset
%T Gansu Province MOD13A2 Enhanced Vegetation Index (EVI) Dataset (2018-2022)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/81fc9fa8-0306-4497-a9db-fc2c0362f5b0
%W NCDC
%R 10.12072/ncdc.gseer.db4130.2023
%A Zhang Yaonan
%K Enhanced vegetation index;vegetation cover;canopy structure;leaf area index
%X Enhanced Vegetation Index (EVI) is an improvement of Normalized Difference Vegetation Index (NDVI), which comprehensively corrects the atmosphere based on imaging factors such as atmospheric molecules, aerosols, thin clouds, water vapor, and ozone. EVI atmospheric correction is divided into three steps, with the first step being cloud removal. The second step is atmospheric correction processing, which includes not only the existing Rayleigh scattering and ozone of NDVI, but also atmospheric molecules, aerosols, water vapor, etc. The third step is to further address the impact of residual aerosols by utilizing the difference between blue and red light through aerosols. Due to the strict atmospheric correction of the input NIR, Red, and Blue, there is no need to use vegetation indices based on the NIR/Red ratio to eliminate multiplicative noise when designing vegetation index equations. This solves the problem of vegetation index saturation and lack of linear relationship with actual vegetation coverage caused by this.  EVI is more sensitive to changes in canopy structure, including leaf area index (LAI), canopy type, vegetation phase, and canopy structure.  Based on the MODIS MOD13A2.005 Enhanced Vegetation Index (EVI) dataset, the segmented images covering Gansu Province were processed using MRT tools and Python language code for batch stitching, projection conversion, cropping, etc., to generate MODIS MOD13A2 EVI data for the Wei River Basin from 2018 to 2022. The spatial resolution of this dataset is 1 km, and the temporal resolution is 16 days.