The normalized difference vegetation index (NDVI) can accurately reflect the surface vegetation coverage. At present, NDVI time series data based on spot / vegetation and MODIS satellite remote sensing images have been widely used in the research of vegetation dynamic change monitoring, land use / cover change detection, macro vegetation cover classification and net primary productivity estimation at various scales. Evi is similar to the normalized difference vegetation index (NDVI) and can be used to quantify vegetation greenness. However, evi corrects for some atmospheric conditions and canopy background noise and is more sensitive in areas with dense vegetation. It contains an "L" value to adjust the canopy background, a "C" value as the atmospheric drag coefficient, and a value from the blue band (b). These enhancements allow the ratio between R and NIR values to be calculated exponentially while reducing background noise, atmospheric noise and saturation in most cases. This research work mainly focuses on post-processing NDVI and evi data, and gives a more reliable vegetation situation of the Qinghai Tibet Plateau in 2013 and 2018 through transformation of projection coordinate system, data fusion, maximum value synthesis method, elimination of outliers and clipping. The spatial resolution of the data is 0.05 °, and the temporal resolution is month.

File naming and required software

one Scale: 13km
two Projection: WGS84
three Data format: TIF format

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Cite as:

Ye, A. (2022). Normalized vegetation index and enhanced vegetation index post-processing products for the Tibetan Plateau in 2013 and 2018. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Terre.tpdc.272734. CSTR: 18406.11.Terre.tpdc.272734. (Download the reference： RIS | Bibtex )

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Support Program

CASEarth:Big Earth Data for Three Poles（grant No. XDA19070000） (No:XDA19000000)

the Strategic Priority Research Program of the Chinese Academy of Sciences (No:XDA19070104) [XDA19070104]

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