What is NDVI, how is it used in agriculture - with which cameras

My Organic Alfalfa NDVI Challenge

Onmy personal journey into precision agricultureand analytics, I came across NDVI in the context of imagery analysis. My objective is toanalyze a 45-hectarefield oforganic alfalfain order to evaluate the fertilizer's effect before and after application. My primary question is: where, what kind, and how much fertilizer should I apply, and what effect will it have on the lucerne crop? I own a Mavic Pro camera with a standard RGB camera. When I asked on Twitter how to proceed, someone suggested using multispectral data to explore a range of Vegetation Indices to help, including NDVI. So, I delveddeep into the rabbit holeto learn more about NDVI.

This aerial perspective captures diverse agricultural fields and natural areas, with highlighted sections indicating specific regions for focused examination. Such raw imagery forms the basis for multispectral analysis and NDVI map generation, crucial for understanding plant health and guiding precise crop management.

The alfalfa fields on my farm, June 2022

The Normalized Difference Vegetation Index (NDVI) is a widely adopted method to evaluate the amount of live vegetation in a specific region, especially in agriculture.

What is NDVI (Normalized Difference Vegetation Index)

Plants are amazing organisms that use sunlight as energy to make their own food. They do this through a process called photosynthesis, which occurs in their leaves. Interestingly, plant leaves not only absorb sunlight, but they also reflect some of it back. This is particularly true for near-infrared light, which is invisible to our eyes but makes up half of the energy in sunlight.

The reason for this reflection is because too much near-infrared light can actually be harmful to plants. So, they have evolved to protect themselves by reflecting this type of light. As a result, live green plants look dark in visible light, but appear bright in the near-infrared spectrum. This is different from clouds and snow, which tend to look bright in visible light but dark in the near-infrared spectrum.

Utilizing the near-infrared reflectance properties of vegetation, these dual NDVI maps reveal plant health variations across an agricultural field. The comparison demonstrates how adjusting the visual dynamic range can emphasize areas of severe stress, shown in red, against healthy green vegetation. Image courtesy of Pix4D, a Swiss company that develops photogrammetry and computer vision software to transform RGB, thermal and multispectral images into 3D maps and models.

Scientists can take advantage of this unique trait to study plants using a tool called NDVI, or Normalized Difference Vegetation Index. NDVI measures the difference between the amount of red and near-infrared light reflected by plants. The more leaves a plant has, the more these wavelengths of light are affected, which can give us important information about plant health and distribution.

NDVI is a way for scientists to use satellite images to study plants and agriculture. By understanding how plants interact with sunlight, we can learn more about the world around us and how to care for our planet.

Summarized: NDVI is a standardized measure of healthy vegetation. It quantifies vegetation by measuring the difference between near-infrared (NIR) and red light. Healthy vegetation reflects more NIR and green light than other wavelengths, but it absorbs more red and blue light. NDVI values always range from -1 to +1.

History of NDVI

In 1957, the Soviet Union launched Sputnik 1, the first artificial satellite to orbit Earth. This led to the development of meteorological satellites, such as the Sputnik and Cosmos programs in the Soviet Union, and the Explorer program in the U.S. The TIROS series of satellites were launched in 1960, and were followed by the Nimbus satellites and the Advanced Very High Resolution Radiometer instruments on the National Oceanic and Atmospheric Administration (NOAA) platforms. NASA also developed the Earth Resources Technology Satellite (ERTS), which became the precurs or to the Landsat program.

The Landsat program was launched in 1972 with the MultiSpectral Scanner (MSS), which allowed for remote sensing of Earth. One early study using Landsat focused on the Great Plains region of the central U.S. Researchers found that solar zenith angle across this strong latitudinal gradient made it difficult to correlate the biophysical characteristics of the rangel and vegetation from the satellite spectral signals. They developed the normalized difference vegetation index (NDVI) as a means to adjust for the effects of solar zenith angle. NDVI is now the most well-known and used index to detect live green plant canopies in multispectral remote sensing data. It is also used to quantify the photosynthetic capacity of plant canopies, but this can be a complex undertaking.

How do you calculate NDVI?

NDVI uses the NIR and red channels in its formula. Satellites like Landsat and Sentinel-2 have the necessary bands with NIR and red. The result generates a value between -1 and +1. If you have low reflectance in the red channel and high reflectance in the NIR channel, this will yield a high NDVI value, and vice versa.

NDVI in agriculture

NDVI has several applications in different sectors. Foresters use NDVI to quantify forest supply and leaf area index, and NASA states that NDVI is a good indicat or of drought. When water limits vegetation growth, it has a lower relative NDVI and density of vegetation. Other sectors that use NDVI include environmental science, urban planning, and natural resource management.

NDVI is widely used in agriculture to monit or crop health and optimize irrigation. Farmers use NDVI for precision farming, to measure biomass, and to identify crops that need more water or fertilizers.

How to use NDVI? Satellite imagery vs. Drone imagery

Which satellite imagery has near-infrared for NDVI? As mentioned before, satellites like Sentinel-2, Landsat, and SPOT produce red and near-infrared images. There are free satellite imagery data sources on the web that one can download to create NDVI maps in ArcGIS or QGIS.

Crop health is a critical aspect of precision agriculture, and NDVI data is a valuable tool for measuring it. Today, the use of agricultural drones has become common practice in pairing NDVI data to compare measurements and identify potential crop health issues. By measuring the difference between near-infrared and red light, NDVI can help farmers optimize irrigation and identify crops that need more water or fertilizers. For example, PrecisionHawk and Sentera provide agriculture drones that can capture and process NDVI data within one day, which is a significant improvement over traditional NDVI techniques that often require long wait times. Researchers have found that NDVI images can even be obtained using standard digital RGB cameras with some modifications, and this approach can be integrated into crop health monitoring systems.

Mobile applications have proliferated in recent years, utilizing NDVI data as a means of monitoring crop health. Doktar' Orbit is one such app that provides farmers with NDVI data presented as health maps to identify any anomalies in their fields. These apps aim to revolutionize farming practices by providing new ways of field scouting and digitalizing agriculture. Remote field monitoring tools based on NDVI technology can save farmers significant fuel costs by reducing the need for frequent field visits, and can help with efficient irrigation management.

What type of (drone) camera for NDVI? RGB & IR-upgraded vs Multispectral

Okay. so this is kind of a hot field I noticed, and the rabbit hole goes deeper and deeper.

Standard RGB cameras are designed to capture red, green, and blue light, while modified cameras can capture a combination of Near Infrared, Red, Green, and Blue light depending on the model. To generate RGB plant health maps showing the "greenness" of crops, one can use a standard RGB camera with specific algorithms in software.

Some companies are selling "fake" Ag or NDVI cameras, which are just regular cameras with an infrared filter removed and a blue filter installed. However, these cameras are inaccurate for radiometric measurements like NDVI because there is too much overlap between col or channels, and they do not have a sens or to account for differences in lighting between visits. As a result, these cameras can only show relative differences in a given area, but not accurately measure NDVI.

A real and calibrated NDVI camera will take into account lighting differences and provide consistent outputs between multiple visits to the same site. So be careful when buying a modified "NDVI camera" that captures Near-Infrared Light, to upgrade drones (already for $400) to capture near-infrared (NIR) imagery in order to carry out vegetation health analysis by NDVI calculation. But please be aware: This is not a real NDVI camera, and this may be misleading. A Sentera camera is already a better option because those are purpose-built and can be calibrated, but they still fall short of a full NDVI system. Multispectral cameras, actual NDVI cameras are expensive, much more costly than the "upgraded RGB/IR cameras". Parrot's Sequoia at $3500. TetraCam ADC Snap $4500, MicaSense's RedEdge $6000+.

Multispectral imagery is important in agriculture because it can provide more detailed and accurate information about crops and soil than traditional RGB cameras.

Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum, using filters or instruments that are sensitive to particular wavelengths. It extends beyond the visible light range to include infrared and ultraviolet light, enabling the extraction of additional information beyond what the human eye can detect with its visible receptors for red, green, and blue. Originally developed for military target identification and reconnaissance, multispectral imaging has been used in space-based imaging to map details of the Earth’s coastal boundaries, vegetation, and landforms. It has also found applications in document and painting analysis.

Here are a few reasons why multispectral imagery is better suited for agricultural applications:

Multispectral imaging typically measures light in a small number of spectral bands, ranging from 3 to 15.

Hyperspectral imaging is a specialized form of spectral imaging where hundreds of contiguous spectral bands are available for analysis. By capturing image data across numerous spectral bands, hyperspectral imaging allows for more accurate identification and analysis of materials than multispectral imaging.

Conclusion

NDVI has revolutionized how we monitor and manage agricultural crops. From its origins in the early Landsat satellite program to today's sophisticated drone-mounted multispectral cameras, this vegetation index has become an indispensable tool for precision agriculture. Whether you're using free satellite imagery or investing in specialized equipment, understanding NDVI helps farmers make data-driven decisions about fertilizer application, irrigation management, and crop health monitoring. As sensor technology continues to advance and becomes more accessible, NDVI and related vegetation indices will play an increasingly important role in sustainable and efficient farming practices.

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NDVI (Normalized Difference Vegetation Index) measures live vegetation by analyzing how plants reflect near-infrared light. Healthy plants reflect a lot of NIR and absorb red light, indicating robust growth. This helps farmers assess crop health, detect stress early, and identify variability across fields, enabling targeted management.

NDVI maps reveal areas of varying plant health and vigor. By identifying zones of lower NDVI, you can precisely target fertilizer application to underperforming spots, rather than treating the entire field uniformly. This optimizes input use, reduces waste, and ensures nutrients are applied where they're most needed to improve yield.

No, standard RGB cameras cannot directly generate true NDVI. True NDVI requires capturing both visible red light and near-infrared (NIR) light, which RGB cameras are not designed to do. While some 'pseudo-NDVI' methods exist, they are not as accurate or reliable for precise agricultural decision-making as data from specialized multispectral cameras.

To generate accurate NDVI data, you need a multispectral camera. These specialized cameras capture light in specific wavelength bands, most crucially the visible red and near-infrared (NIR) bands. Drones equipped with these multispectral sensors can fly over fields to collect high-resolution imagery for detailed vegetation indexing.

Healthy, photosynthetically active plants strongly absorb red light for energy production but vigorously reflect near-infrared (NIR) light to avoid overheating. This distinct pattern – high red absorption and high NIR reflection – is what NDVI leverages. Stressed or unhealthy plants show a different pattern, which allows NDVI to differentiate their health status.

Higher NDVI values (closer to 1) indicate denser, healthier, and more photosynthetically active vegetation, suggesting robust growth. Conversely, lower NDVI values (closer to -1, or near 0 for bare soil) suggest stressed, sparse, or unhealthy plants, or non-vegetated areas. This helps pinpoint problem zones for intervention like irrigation or pest control.

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• Crop Health Monitoring | Doktar (2025) - Doktar - Doktar Crop Health Monitoring provides satellite-based, real-time insights into crop health and growth stage, enabling...
• State of Major Vegetation Indices in Precision Agriculture Studies Indexed in Web of Science: A Review (2023) - Dorijan Radočaj, Ante Šiljeg, Rajko Marinović, Mladen Jurišić - This review paper analyzes the prevalence and application of major vegetation indices, including NDVI, in precision agriculture studies.
• NDVI and Beyond: Vegetation Indices as Features for Crop Recognition and Segmentation in Hyperspectral Data (2025) - Andreea Nițu, Corneliu Florea, Mihai Ivanovici, Andrei Racoviteanu - This paper investigates the distinctiveness and discriminative power of NDVI and other vegetation indices for crop recognition and segmentation in agriculture.
• Landsat Normalized Difference Vegetation Index (2025) - U.S. Geological Survey (USGS) - This authoritative USGS page explains Landsat NDVI, its calculation, and its utility in quantifying vegetation greenness and health.