The Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, typically, but not necessarily, from a space platform, and assess whether the target being observed contains live green vegetation or not.

The NDVI is calculated from these individual measurements as follows:

NDVI= (NIR-Red) \ (NIR+Red)

The NDVI value ranges between -1.0 and +1.0. Generally speaking, NDVI shows a functional relationship with vegetation properties (e.g. biomass). NDVI is directly related to the photosynthetic capacity and energy absorption of plant canopies. Sometimes, NDVI value could be positive in more in more densely populated urban areas too. This could be because NDVI is calculated using the normalized difference and while doing so it may loose some spectral information. In addition, the calculation of the NDVI values are sensitive to various factors including: atmosphere ( water vapor and aerosols ) , clouds (thick clouds, thin clouds, cloud shadows), soil (moisture contents because of precipitation and evaporation), anisotropy, spectral signature, Modifiable Areal Unit Problem (MAUP), and more.

There are a number of derivatives and alternatives to NDVI that have been proposed in the scientific literature to address the limitations of NDVI. These alternatives attempt to include intrinsic correction(s) for one or more perturbing factors.

Let us learn different methods to deal with NDVI.


  1. Simple Methods to calculate NDVI.
  2. Using Landsat Images to calculate NDVI (Study Area: India)


  1. Develop an App to Calculate Mean NDVI.
  2. Create a Time-series to Display NDVI Values in Different Regions Across Images
  3. Calculate Annual Time Series NDVI – (Study Area: Nigeria)
  4. Develop a Harmonic Model: Original vs Fitted NDVI values
  5. Create a Time-series to Display NDVI Mean Day of Year for Multiple Years using MODIS Data
  6. Create a chart of NDVI over time using Widgets
  7. Use NDVI to Determine Bare-Land


  1. Use Reducer to Observe the difference between weighted and unweighted mean of the NDVI image clipped to the region.
  2. Animate the NDVI derived from MODIS 16-Day Global 250-m through the year using Google Earth Engine.
  3. Calculate and Export Annual 90th Percentile NDVI for years 1985-2019 – (Study Area: Turkey)
  4. Animate 30m Landsat images generated 90th percentile Annual NDVI from 1999 to 2018 (Study Area: South America)
  5. Calculate NDVI for a Single Month over 36 years (1985-2019)
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