What NDVI is and how it helps in the field

NDVI (Normalized Difference Vegetation Index) is a satellite index that helps read a crop’s vigor without setting foot in the field. It doesn’t replace scouting or agronomic judgment, but it’s a very efficient tool for deciding where to look first when there are many hectares, few technicians and little time available in the week.

What NDVI measures

The index compares how vegetation reflects visible light (red band) against near-infrared light. A healthy plant absorbs a lot of red light for photosynthesis and strongly reflects near-infrared; a plant under stress, bare soil or stubble behaves differently. The result is a number between -1 and 1: values close to 1 indicate dense, vigorous vegetation, values close to 0 indicate bare soil or sparse vegetation, and negative values usually correspond to water or clouds.

In practice you don’t need to memorize the formula. What matters is reading the map as a guide for priorities: more even zones, sectors with weaker development, or areas worth checking before applying a blanket recommendation to the whole field.

Where the data comes from and how often it updates

Segesio combines two satellite sources: Agromonitoring as the primary source and Copernicus/CDSE as a backup, especially for small fields — under one hectare — where the primary satellite’s resolution may not be enough. The image updates based on the satellite’s pass over the area and cloud cover: it isn’t real-time data, it’s the most recent photo available, and it’s worth reading it with that limitation in mind.

When NDVI adds real value

NDVI becomes useful when it’s connected to scouting, photos, weather, crop, activity history and soil sampling. A spot on the map can mean water, a nutritional deficiency, a pest, compaction, or simply different management in that part of the field; the image points, but field data confirms. Used alone, an NDVI map is a visual curiosity. Used alongside the rest of the operation, it becomes a way to prioritize where to go first.

Real example: from a spot on the map to a scouting decision

At Granja Novo, during the 2025/26 summer (December to March), three pear fields showed a distinctly different pattern despite sharing the same crop and sitting a few meters apart. The data below comes from CDSE, the satellite source we use for this crop.

This kind of tracking, repeated through the season, is what separates a nice satellite image from a documented agronomic decision.

Common mistakes when reading an NDVI map

The most common mistake is treating NDVI as a diagnosis in itself, instead of a signal that needs to be confirmed in the field. Another frequent mistake is ignoring the crop’s growth stage: a "low" value can be completely normal in a crop that just emerged, and comparing that same field against another one at a different stage leads to wrong conclusions. It’s also common to chase false positives caused by clouds, shadows or bodies of water inside the field, which the index can confuse with plant stress if you look at it without that context.

A third mistake, less talked about but just as costly, is looking at NDVI from a single date instead of its evolution. A single value says little on its own; the trend over several weeks — whether a sector is improving, stalling or getting worse relative to the rest of the field — usually carries much more information than one day’s isolated image. That’s why it’s worth reviewing a field’s image history, not just the most recent one, before deciding on a specific scouting visit.

NDVI and the rest of precision decisions

NDVI pays off more when it’s read alongside other layers of information already available in the operation: the field’s fertilization history, the latest soil sample results, the crop and its cycle, and the weather conditions of the previous weeks. A zone with low NDVI after a late frost tells a different story than the same zone with low NDVI in a field that never received the nutrient the soil analysis flagged as limiting.

This cross-reading is what separates precision agriculture as a concept from precision agriculture as a daily practice. Having the satellite map available isn’t enough: the value shows up when that map connects to the rest of the data the operation already generates week after week, and when the decision that follows — scout, sample, adjust a rate — gets logged alongside the data that prompted it.

How Segesio approaches it

Segesio lets you view the field with NDVI, log georeferenced observations during scouting, and keep that follow-up tied to the rest of the operation: activities, applied inputs, the period’s weather and per-field profitability reports. That way precision agriculture stops being an isolated image and becomes part of full production traceability.

See precision agriculture in Segesio ERP

Frequently asked questions