Remote Vegetation Indices: How They Work

Every surface or object on earth has unique characteristics which distinguish it from others. In Remote sensing, one of such characteristics is reflectance. So, vegetation indices are a collection of surface reflectance at more than one wavelength that is created to bring out the properties of specific vegetation.

What does vegetation index mean?

The scientific community has agreed on more than 100 vegetation indices. However, only a few of them have proven useful and have undergone significant testing. Dr Gregory P. was the great mind who first categorized the most vital vegetation types and which indices best represent the different types. At the time of this creation, he was working in the Global Ecology Department at the Carnegie Institute, Washington. His choices were based on different criteria like usefulness, expert approval, etc. Some of the indices and types include Broadband Greenness, Light Use Efficiency, Canopy Water Content, Narrowband Greenness, Canopy Nitrogen, Leaf Pigments, and Dry or Senescent Carbon.

All the different groups of indices have different ways to estimate whether any property of the vegetation is present. The indices do not have similar validity for various field states and characteristics, even those within the same group.

How does it work?

Vegetation indices can be measured because different surfaces or objects do not have the same absorption and reflection properties of electromagnetic radiation (ER) measured using satellite sensors. For example, because of these properties, a tree in the Saraha desert and a crop in a rainforest will have different values despite conditions and wavelengths being similar. Also, they can have different indices depending on where the plants are located in the same field. This is because some could be in the shade, under the sun, or elsewhere.

The satellite sensor type is one of the main deciding factors when creating indices. That is why all satellites used for monitoring indices must have hyperspectral and multispectral sensors. Every index uses a blend of different wavelengths collected at a specific time and on the same land to measure parameters.


The Normalized Difference Vegetation Index remote sensing technique to help identify the health of vegetation. That is why NDVI-based field monitoring plays a key role in decision-making and farm yield.

As a foundation for understanding NDVI, we look at the spectral properties of vegetation. It shows that chlorophyll and photosynthesis presence causes light absorption in the red band of the EM spectrum. While the NIR region indicates internal cellular structure or biomass vegetation. 

In general dense vegetation reflects lots of NIR light but very little red as it is absorbed instead. Conversely, when the vegetation is sparse or not so healthy, then we see a decrease in the NIR reflectance and an increase in the reflectance of the red band as there is less chlorophyll to absorb the emitted red light.

NDVI integrated the data available in the red and NIR bands into one value. This is done by finding the difference between the red spectral band from the NIR. Now, the answer is divided by the sum of red reflectance and NIR value. The negative sign on the numerator ensures that regardless of the NIR and red values, the numerator always calculates out to a number less than the denominator. It implies that NDVI will always equate any value between -1 and +1.

Healthy vegetations have a high NIR and low red reflectance value. Here, the NIR value dominates the NDVI equation. The NDVI will therefore move towards +1. In less healthy vegetation, red reflectance is more significant, thus decreasing the overall value of NDVI. However, it will still remain positive.


The EVI stands for Enhanced Vegetation Index and is particularly designed to look at areas with dense vegetation. It allows us to distinguish some of the variations in those highly dense fields. Like many other vegetation indices, it makes use of NIR and red bands. It also has a blue band which helps to highlight some of those differences in highly vegetated areas, soil and atmospheric reflectance.

MSAVI is the acronym for Modified Soil Adjusted Vegetation Index. This index lifts the limit on applying NDVI to farms with significant bare soil. With NDVI, there is enormous interference from soil reflectance, making the values less accurate. MSAVI aims to reduce bare soil influence and enhance the range of vegetation signals.

OSAVI stands for Soil-adjusted Vegetation Index and is useful in regions with low vegetative cover. The soil reflectance often influences NDVI, which is when OSAVI comes into play. It is a modification of the NDVI, which factors in soil brightness.