NIR and Vegetation – The Basics

NIR and Vegetation – The Basics

The following is an excerpt from Utah State University Cooperative Extension. I posted this as a resource reference for FlightRiot users to get a quick understanding of the relationship between NIR and vegetation. Upon reading this post you can easily understand the vast uses of NIR imagery in agriculture, vegetation management, resource management and potentially even highly targeted pesticide application.

NIR is a small portion of the much larger region called infrared (IR), located between the visible and microwave portions of the electromagnetic spectrum. NIR makes up the part of IR closest in wavelength to visible light and occupies the wavelengths between about 700 nanometers and 1500 nanometers (0.7 µm – 1.5 µm). NIR is not to be confused with thermal infrared, which is on the extreme other end of the infrared spectrum and measures radiant (emitted) heat.

electromagnetic spectrum


What Does NIR Tell US?

Since NIR has longer wavelengths than visible light, it exhibits peculiar properties that can be exploited for remote sensing applications. Some of the information that can be obtained from NIR is crop stress (water and nutrient stress being the most common) and weed/pest infestations.

Reflected Bands of colorChlorophyll pigment absorbs most energy at about 650 nm (red) and around 450 nm (blue). Other pigments absorb more visible wavelengths, but the most absorption occurs in the red and blue portions of the spectrum. This absorption removes these colors from the amount of light that is transmitted and reflected, causing the predominant visible color that reaches our eyes as green. This is the reason healthy vegetation appears as a dark green. Unhealthy vegetation, on the other hand, will have less chlorophyll and thus will appear brighter (visibly) since less is absorbed and more is reflected to our eyes. This increase in red reflectance along with the green is what causes a general yellow appearance of unhealthy plants.

Another feature of vegetation is the strong reflectance within the NIR. Since NIR is not absorbed by any pigments within a plant, it travels through most of the leaf and interacts with the spongy mesophyll cells. This interaction causes about half of the energy to be reflected and the other half to be transmitted through the leaf. In plants with turged and healthy mesophyll cell walls and in dense canopies, more NIR energy will be reflected and less transmitted. This cell wall/air space interaction within these cells causes healthy vegetation to look very bright in the NIR. In fact, much more NIR is reflected than visible.

By monitoring the amount of NIR and visible energy reflected from the plant, it is possible to determine the health of the plant.

High NIR reflectance / Low visible reflectance = Healthy

Low NIR reflectance / High visible reflectance = Unhealthy

reflectance from visible to NIRBecause of the wider range of reflectance between healthy and unhealthy vegetation within the NIR region, sensors that detect within this region are much more sensitive to subtle changes in plant health. These sensors use silicon based arrays to measure the amount of NIR and visible energy reflected. These devices will often convert the result into one of several indicies such as the normalized difference vegetation index (NDVI). In this way, the health of vegetation can be constantly monitored.



  1. daniel sepulveda mejias

    then at night we could use thermal cameras and find other patterns?
    I am not a specialist in the field dela vegetation from the air, but on the way we learn.

  2. Beerwiser

    Is there any database or information regarding specific plant spectral reflections? There are massive plant databases, just wondering if anyone has cataloged spectral reflections. Lastly, cameras for NIR. Where to buy, are they light enough for our hobby uav’s etc.

    • Admin

      I’m not sure of a specific database that is available that defines the spectral signature of plants en mass. However, if you look for specific plants you can probably find research that has been completed and published. I think a practical solution will include a spectral signature as well as pattern matching, which means relatively high resolution imagery. Perfect for sUAS/UAV work!
      I’d be happy to help you work on solving a specific use case. We can devote a section of the site to it and treat it as a project. I created a vegetation management category and also and agriculture category to accommodate. If you can define the project goal, that will be a great start. Then I’ll hit you with a bunch of questions and we can get it started based on your answers.

  3. Beerwiser

    So how will NIR look on a area of mixed vegetation such a a native grass land compared to single crop such as canola? Is it also possible to pick out certain plants like invasive species? Sorry for the newbie questions as this is above my pay grade ATM :).

    • Admin

      There are no questions that are too NOOB! We are all learning as we go.
      NIR reflectance intensity is related to the internal structure of the vegetation whereby healthy vegetation reflects a lot of NIR (and VIS Green). Various Vegetation Indices (VI) could be used individually or in conjunction to estimate infestation location, area, and severity if you have two essential criteria:
      1)suitable differences in spectral
      reflectance or texture between weeds and their background soil or plant canopy
      2)appropriate spatial and spectral resolution to detect the presence of weed plants

      One thing that UAVs have going for them is the ability to capture relatively high resolution (spatial) data very efficiently. When compared to Landsat 5 for example, it captures 30M resolution. The ability to capture a resolution of less than the minimum expected size of the weed patches greatly improves the chances of using VI to successfully differentiate between vegetation types. I have some ideas that I’d be happy to discuss with you. You can most likely figure out a functional workflow for your needs.

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