NDVI Calculator

The Normalized Difference Vegetation Index, or NDVI, is one of the most widely used metrics in remote sensing. It measures the density and health of vegetation by comparing how much red light a surface absorbs with how much near-infrared light it reflects. Healthy leaves absorb most red light through chlorophyll and scatter near-infrared light through spongy leaf tissue, so vigorous plants produce high NDVI values.

Our calculator uses the standard formula: NDVI = (NIR - Red) / (NIR + Red). Both inputs should be surface reflectance values, which range from 0 to 1. If you only have percent reflectance, switch the input unit to percent and the tool converts it automatically. The result always falls between -1 and 1, where negative values usually mean water or shadow and values above 0.6 indicate dense, healthy vegetation.

How to Interpret NDVI Values

Interpreting NDVI is straightforward once you know the scale. Values below zero often indicate open water, deep shadow, snow, or built-up surfaces because these targets reflect more red light than near-infrared light. Bare soil and rock typically sit between 0.0 and 0.2 because they reflect red and NIR light in roughly equal amounts.

Sparse vegetation such as dry grass or young crops usually falls between 0.2 and 0.4. Moderate vegetation, including pastures and mixed cropland, ranges from 0.4 to 0.6. Values from 0.6 to 0.8 signal healthy, dense vegetation, while scores above 0.8 are typical for rainforests, irrigated fields, or wetlands with thick canopies.

What Are Red and NIR Bands in Satellite Imagery?

Satellite and drone sensors capture light in discrete wavelength ranges called bands. The red band is usually centered near 650 nanometers, where chlorophyll strongly absorbs light. The near-infrared, or NIR, band is centered near 850 nanometers, where healthy leaf cells reflect strongly. By combining just these two bands, NDVI removes many lighting and atmospheric effects and isolates the vegetation signal.

Free sources such as Landsat, Sentinel-2, and MODIS provide red and NIR bands for any point on Earth. Many agriculture drones also capture NIR with dedicated cameras. The key requirement is that both inputs use the same scale: either top-of-atmosphere reflectance or atmospherically corrected surface reflectance. Mixing raw digital numbers from different sensors without calibration will give misleading results.

Common NDVI Use Cases in Agriculture and Ecology

Farmers and agronomists use NDVI to detect crop stress, plan irrigation, and estimate yield. A dropping NDVI across a field can reveal nutrient deficiency, pest pressure, or water stress before it is visible to the eye. Conservation groups use NDVI time series to monitor deforestation, reforestation success, and wetland health. Urban planners use it to map green space and heat-island mitigation.

Drone operators often fly multispectral cameras before and after fertilizer or pesticide application to measure treatment response. Because NDVI is a ratio, it is relatively stable across different times of day and sun angles, making it ideal for comparing images taken weeks or months apart.

NDVI Limitations and Common Mistakes

NDVI saturates over very dense vegetation, where it becomes less sensitive to small changes in biomass. It is also sensitive to soil brightness, especially in arid regions with sparse cover. Clouds, shadows, and atmospheric haze can distort values, which is why analysts usually apply atmospheric correction before calculating NDVI from satellite images.

Another common mistake is using raw pixel values, or digital numbers, instead of reflectance. Digital numbers depend on sensor gain settings and do not represent physical reflectance. Always convert to reflectance first, or use a sensor-specific scaling factor provided by the data distributor.

NDVI Values and Surface Types

Frequently Asked Questions