Everything About NDVI - 'Crop Health' - Imaging

Misconceptions about UAV-collected NDVI imagery and the Agribotix experience in ground truthing these images for agriculture

First off, what is NDVI? Substantial confusion exists on this subject, but it is actually quite straightforward. We consistently see high profile websites refer to NDVI as a measure of chlorophyll content, water content, or something else entirely, but NDVI is simply a ratio of near infrared (NIR) reflectivity minus red reflectivity (VIS) over NIR plus VIS.

NDVI=(NIR-VIS)/(NIR+VIS)

Specifically, NDVI was developed by a NASA scientist named Compton Tucker in a 1977 paper entitled, “Red and Photograghic Infrared Linear Combinations for Monitoring Vegetation.” He examined 18 different combinations of NIR (Landsat MSS 7 800-1100 nm), red (Landsat MSS 5 600-700 nm), and green (Landsat MSS 4 500-600 nm) and compared these results with the density of both wet and dry biomass to in an attempt determine which combination correlated best. His findings were that NIR/red, SQRT(NIR/red), NIR-red, (NIR-red)/(NIR+red), and SQRT((NIR-red)/(NIR+red)+0.5) were all very similar for estimating the density of photosynthetically active biomass.

This isclassic NDVI image of the Earth taken by one of the Landsats. The NDVI equation was developed with assessing the amount of vegetation on Earth using, at the time, brand new satellite technology. The original Landsat had 4 bands and a 60 m resolution.

These results are not surprising. Plants reflect strongly in the near infrared because of a spongy layer found on the bottom surface of the leaf, but not strongly in the red (plants are green after all, meaning they reflect green light). Soil, on the other hand, reflects both. However, when a plant becomes dehydrated or sickly, the spongy layer collapses and the plant ceases to reflect as much NIR light. Thus, a linear combination of the NIR reflectivity and red reflectivity should provide excellent contrast between plant and soil and even healthy plants and sickly plants. It turns out which combination is not particularly important, but the NDVI index of (NIR-red)/(NIR+red) was particularly effective at normalizing for different irradiation conditions and Compton had to pick one, so it stuck. Compton proceeded to publish more than a hundred papers using this index, including a 1979 paper following corn and soybean development that we will discuss later.

The basic principle of NDVI relies on the fact that, due the their spongy layers found on their backsides, leaves reflect a lot of light in the near infrared, in stark contrast with most non-plant object. When the plant becomes dehydrated or stressed, the spongy layer collapses and the leaves reflect less NIR light, but the same amount in the visible range. Thus, mathematically combining these two signals can help differentiate plant from non-plant and healthy plant from sickly plant.

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