Multispectral Imaging: Uses and Application

by Afiq Bahruddin

Explore more on the workings behind a multispectral and how it is being used especially in agriculture sector

Last month, the brand new Phantom 4 Multispectral was launched by DJI. The latest addition to the Phantom series surely excites the industrial drone bodies and companies.

Phantom 4 Multispectral is built to optimise the operation in agriculture, with built-in multispectral cameras. Many have asked what exactly is multispectral sensor and how does it work in agriculture and other industries.

In this article, we will explore more on the workings behind a multispectral and how it is being used especially in agriculture sector.

What is a Multispectral Sensor

Multispectral sensors are a type of sensor that is able to collect the visible electromagnetic wavelengths as well as wavelengths that fall outside the visible spectrum as opposed to the standard visual sensor that collects red, green and blue wavelengths of light.

The non-visible electromagnetic waves that can be observed via multispectral sensor includes near-infrared radiation (NIR), short-wave infrared radiation (SWIR) and others.

Every surface will reflect different composition of wavelength and this reflected waves are the ones that can be observed through multispectral camera. That data can be compared to other nearby objects to understand the crucial differences between them.

Beside Phantom 4 Multispectral which is equipped with the multispectral sensor, other drones also can be mounted with third-party multispectral sensor such as sensors from Sentera.

Application in Agriculture

Drone multispectral imaging is now getting more and more adoption in agricultural nature of business. This is due to the capability of multispectral imagery is able to discern the health condition of crops and plants.

As mentioned earlier, as all surface would reflect waves differently from each other, healthy leaves will reflect different spectrum of electromagnetic waves than the leaves on dead or unhealthy plants.

There are a lot of types of data set images that can be indexed using multispectral data, amongst them; NDVI, NDRE, SAVI and many more types of vegetation index that can be used in discerning crops health.

Vegetation indices are usually measured using the aforementioned vegetation reflective properties. The indices are used to analyze various ecologies.

Vegetation indices are constructed from reflectance measurements in two or more wavelengths to analyze specific characteristics of vegetation, such as total leaf area and water content.

Vegetation interacts with solar radiation differently from other natural materials, such as soils and water bodies. The absorption and reflection of solar radiation is the result of numerous interactions with different plant materials, which varies considerably by wavelength.

Water, pigments, nutrients, and carbon are each expressed in the reflected optical spectrum from 400 nm to 2500 nm, with often overlapping, but spectrally distinct, reflectance behaviours. These known signatures allow scientists to combine reflectance measurements at different wavelengths to enhance specific vegetation characteristics by defining vegetation indices.


NDVI is one of the most-used vegetation indices up-to-date. It describes the vigour level of the crop and it is calculated as the ratio between the difference and the sum of the refracted radiations in the near-infrared and in the red, that is as (NIR-RED)/(NIR+RED).

The interpretation of the absolute value of the NDVI is highly informative, as it allows the immediate recognition of the areas of the farm or field that have problems. The NDVI is a simple index to interpret: its values vary between -1 and 1, and each value corresponds to a different agronomic situation, regardless of the crop

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