NDVI and Your Farm: Understanding NDVI for Plant Health Insights


Source: Sentera, LLC

NDVI and Your Farm: Understanding NDVI for Plant Health

Understanding your crops’ health status isn’t the easiest thing to do. Sure, you can use the “eye test”, and a number of foliar contact and direct measurement techniques. But, what if there was an easy, fast and efficient way to see the health of plants and their status and progress over time? That’s where Normalized Difference Vegetation Index (NDVI) data comes in.

NDVI is often discussed without much explanation. Even at Sentera, we are proud to talk about our ability to compute NDVI in real time, but sometimes we forget to start by explaining what NDVI is. That can leave everyone feeling a bit confused. We don’t want that. We want you to know what data you’re getting, and help make it easier to manage your fields. So let’s clear it up.

What is NDVI?

In simple terms, NDVI is a measurement of plant health based on how a plant reflects light (usually sunlight) at specific frequencies. To be more specific, NDVI is a measurement of the reflectivity of plants expressed as the ratio of near-infrared reflectivity (NIR) minus red reflectivity (VIS) over NIR plus VIS.

NDVI Plant Health Equation | Sentera

The equation for NDVI was developed several decades ago to make use of satellite imagery in agriculture. The way the equation is built makes it insensitive to overall brightness or darkness of light — it essentially tracks the ratio of NIR to red reflectivity, which doesn’t change with overall brightness.

NDVI works because when sunlight reaches a plant, certain wavelengths are absorbed while others are reflected. Chlorophyll strongly absorbs visible light while the cell structure of leaves strongly reflects near-infrared light. A spongy layer along the bottom of a plant causes these reflections. When a plant becomes dehydrated, sick, affected by disease, etc. this spongy layer deteriorates and the plant absorbs more of that near-infrared light rather than reflecting it. Conversely, when near-infrared light hits a leaf on a healthy plant, it is reflected back. So, looking at how NIR varies compared to red light provides an accurate indication of chlorophyll, which correlates to plant health.

The equation explained above will always result in a NDVI plant health value between -1 and +1. A number between -1 and 0 suggests an inanimate or dead object, like roads, buildings, or dead plants. A NDVI plant health rating between 0 and 0.33 indicates unhealthy or stressed plant material, 0.33 to 0.66 is moderately healthy, and 0.66 to 1 is very healthy. These numbers are just rules of thumb, and vary based on type of plant and other conditions. But that’s enough science for now.

How Sentera Delivers NDVI Plant Health Insights

During the growing season, efficient use of time saves money, and better decisions grow profits. Sentera drones, sensors, software and analytics allow you to see NDVI plant health values in real time. You simply fly your drone, upload the data to AgVault, and get instant plant health maps. Some Sentera systems even let you see instantaneous, LiveNDVI™ video. NDVI is only available from sensors that can sense near-infrared (NIR) light. Synthetic NDVI is not real NDVI.

NDVI Values and Plant Health Maps

As mentioned, NDVI plant health values are between -1.0 and +1.0 But how does this translate to the colorful NDVI maps you’ve probably seen? Basically, certain ranges of NDVI values are mapped to a set of colors. One of the most common color maps is the “red-green” NDVI color map. In this map, NDVI plant health values from -1 to 0 range appear red, 0.0 to 0.33 are orange-ish red or yellow, 0.33 to 0.66 tint green, and above 0.66 appear green. There isn’t a “standard” color map. Some people don’t like these colors, some people want more colors and some want fewer. AgVault’s NDVI Toolbox™ lets you recolor, adjust, add or remove colors, and change color palettes dynamically for exactly this reason — not everybody liked the colors we chose, so now we let them pick their own!

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