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.
The data source of this dataset is MOD13A2-v005 version data, which is sourced from the NASA official website（ https://ladsweb.modaps.eosdis.nasa.gov ）.
(1) Trial run the MRT tool to generate prm files for MOD13A2 EVI data concatenation and projection conversion, with a spatial resolution of 1000 m.
(2) Generate a batch file using MATLAB language program to call the MRT tool and run it;
(3) Utilize the vector boundary of Gansu Province, perform batch cropping using Python, and finally output and save in GeoTIFF format.
This dataset has the same quality as the source dataset MODIS MOD13A2.005. The effective range of data is -2000 to 10000, with a scaling factor of 0.0001 and a padding value of -3000.
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CSTR:11738.11.NCDC.GSEER.DB4130.2023
10.12072/ncdc.gseer.db4130.2023