Satellite crop monitoring with advice for each field

What the satellites measure

Satellite crop monitoring rests on a bit of plant physiology. Healthy leaves absorb most of the red light that reaches them, because chlorophyll uses it for photosynthesis, and they reflect a lot of near-infrared light because of the way the cells inside the leaf are arranged. When a plant runs short of water or nitrogen, or a disease starts to take hold, that balance shifts. The change often shows up in the near-infrared before anyone walking past would see the leaves change color.

Optical satellites

Most crop monitoring software starts from public missions. The European Space Agency's two Sentinel-2 satellites photograph the same spot about every five days between them. Their visible and near-infrared bands have 10 m pixels, and their red-edge and shortwave-infrared bands have 20 m pixels. Landsat 8 and 9, run by NASA and the USGS, add 30 m imagery and a thermal band. Commercial constellations sell near-daily imagery at around 3 m, at a higher price per acre. All optical satellites share one weakness: they can't see through cloud.

Radar satellites

Radar satellites such as Sentinel-1 send out their own microwave pulse and record what bounces back, so they work through cloud, smoke and haze, and at night. The return depends on the structure of the canopy and on the moisture in the plants and the top few centimeters of soil. That's why the radar vegetation index and radar soil moisture are the views to rely on in a wet season.

NDVI, NDRE and the other indices, briefly

An index combines two or more bands into one number per pixel. NDVI is the best known: (NIR − Red) / (NIR + Red), which gives a value between −1 and +1. Bare soil sits near the bottom of the crop range and a dense, healthy canopy near the top. We've written a separate walkthrough of the NDVI formula and how to calculate it, so here is only the short version.

The mistake we see most often is reading NDVI late in the season. Once a maize or sugarcane canopy closes, NDVI saturates: most of the field reads high and the differences that matter flatten out. NDRE keeps its range for longer, which is why agronomists switch to it for nitrogen decisions. Our post on NDVI vs NDRE goes through the differences, and the one on EVI vs NDVI explains why the two disagree on thick canopy.

Revisit, resolution and cloud: what to expect

Every vendor quotes a revisit time. The number is real, but a revisit is a chance of an image, and whether you get a usable one depends on the weather. In a dry summer you'll get most Sentinel-2 passes. In a tropical rainy season you can go weeks without a clear optical view, which is the gap radar fills. When you see "real time crop monitoring" advertised from satellites, read it as "updated each time a satellite gets a look at the field". With public satellites that's a few times a week at best.

Resolution matters more than people expect on small fields. A 10 m pixel covers 100 square meters, about a fortieth of an acre, so a 2-acre plot holds roughly 80 pixels. That's plenty to see a weak corner. On a strip narrower than about 30 m, though, many pixels straddle the boundary and mix your crop with a track or the neighbor's field, so treat readings along the edges with care.

Reading the field report through a season

What you look for in the report changes as the crop grows. Roughly, by stage:

• Around emergence the field is mostly soil. SAVI and the crop health map show gaps in the stand, waterlogged patches and areas that came up late, which is when replanting decisions get made.
• From rapid growth to canopy closure, compare each pass with the one before. A zone that falls behind its neighbors is the first place to walk. Nitrogen decisions happen here too, so check NDRE alongside NDVI.
• Around flowering and grain fill, water stress costs the most yield. Watch the moisture views and the irrigation plan closely, and check the pest risk after warm, humid spells.
• Toward harvest the canopy dries down and greenness drops on purpose. The yield estimate and harvest date matter more than the health map now.

Acting on the report is mostly a question of where you spend your time. Go to the stressed zone, dig a hole, look at the roots and check the drip line or nozzle. On the farms in our case studies, much of the water saving came from days when the irrigation plan said a block needed none. The AI farm advisory page explains how the water, fertilizer and pest numbers are worked out, and our guide on how to monitor crop health using satellites goes through the health map step by step.

Satellites, drones, scouting and soil probes compared

None of these fully replaces the others, and most well-run farms use two or three of them.

Drones make sense when you need plant-level detail on a small area, such as a research plot or a high-value orchard block, and our piece on drone NDVI mapping covers that side. In the US, commercial drone flights also need an FAA Part 107 certificate. Probes give the most precise root-zone reading at one spot, and IoT crop monitoring systems pair well with satellite maps, which show whether the probe sits in a representative part of the field. Scouting is still the only way to confirm which pest or disease you're dealing with.

How to choose crop monitoring software or a service

Almost every crop monitoring app shows a greenness map now. The differences are in what comes with it. These are the questions we'd ask any vendor, including us:

• Which satellites does it use, and is there radar for cloudy weeks?
• What resolution is each layer delivered at? On Sentinel-2, a 10 m band and a 20 m band show different amounts of detail.
• Does it give an action in units you can use, such as millimeters of water or kilograms of a nutrient, or only a colored map?
• Will it ever recommend doing nothing? Much of the saving comes from weeks when a block needs no water or fertilizer.
• How do fields get in? Drawing works for a few, but KML, KMZ and shapefile uploads save hours on a large farm.
• Can you share a field with an agronomist or buyer who has no account, in their language?
• Is there an API if you want the data in your own farm management system?
• Is it priced per acre, per field or per user, and what happens when you add land mid-season?

If you're comparing options, our roundup of crop scouting and crop monitoring apps covers the wider market. Farmonaut's farm plans are priced by the area you monitor and listed on the pricing page. Agribusinesses, lenders and large estates usually talk to our team about volume.

Getting started with remote crop monitoring

1. Pick two or three fields to begin with, ideally one you know well and one that worries you. You'll learn to read the maps faster on ground you know.
2. Get the boundaries in. Draw them on the map, or upload a KML, KMZ or shapefile.
3. Write down the crop, variety and sowing date for each field. Any advice that depends on growth stage is only as good as that date.
4. Look at the first map as a baseline. It often shows old patterns, such as a compacted headland, a wet hollow or the line of a fence that was taken out years ago.
5. Walk the weakest zone after the first report and note what you find. That's your own check on the data.
6. After a month, compare what the report recommended with what you did, then add the rest of the farm.

Mapping a field takes about a minute, so setup is the quick part. Learning to read the maps against what you see on the ground takes a few weeks.