# Satellite Imagery for Agriculture: Monitoring Hundreds of Fields, Choosing Data Sources and Acting on Alerts

> Satellite imagery for agriculture: free Sentinel-2 data, cloud gaps, satellite vs drone, and a six-step workflow to flag which fields to visit first.

Source: https://digitalbridge.com.tr/en/blog/satellite-imagery-agriculture-monitoring.html
Publisher: Digital Bridge Teknoloji (https://digitalbridge.com.tr)
Topic: Data & Analytics
Published: 2026-10-29 · Updated: 2026-10-29

Satellite imagery for agriculture means using multispectral images captured by orbiting satellites at regular intervals to track crop development, stress and change on every field as a time series. It is built for watching hundreds of fields to the same standard at low cost; close inspection is then left to drones and field visits.

## Key takeaways

- Satellite monitoring adds most value for food companies, cooperatives and consultants managing many scattered fields.
- Sentinel-2 data from the EU's Copernicus programme is free, with 10 m resolution and a 5-day revisit time.
- According to NASA, only 30% of skies over land are completely cloud-free; the Sentinel-1 radar satellite fills cloudy-season gaps.
- The satellite shows where to look; drones, soil moisture sensors and field visits reveal the cause and the extent.
- The real value comes from using per-field time series to prioritise the field team's visit list.

## Who actually needs satellite imagery in agriculture?

For a single grower with one field, satellite monitoring is often more tool than the job needs; its value appears when the area under management is large and scattered. A food company running contract farming has to follow hundreds of growers across dozens of villages. A cooperative wants to know which member's crop is in trouble before sending an agronomist. An agronomy consultancy serving many clients, or a large estate with land many kilometres apart, cannot visit every field every week.

In these organisations the field team's time is the scarcest resource. Visit order is usually set by the driving route, by whoever phoned in a complaint, or by habit. When the problem field sits at the bottom of the list, intervention comes late. Satellite imagery is a low-cost way to answer "which fields should we visit today?" with data rather than guesswork.

This article is about wide-area, portfolio-level monitoring. For a high-resolution crop health map of a single field, see our guide to [drone NDVI analysis](https://digitalbridge.com.tr/en/blog/drone-ndvi-analysis.html); for estimating tonnage before harvest, read [drone crop yield estimation](https://digitalbridge.com.tr/en/blog/drone-crop-yield-estimation.html).

## What does crop monitoring without satellite data cost?

In farming, inputs applied in the wrong place and stress noticed too late turn straight into money. Water is the biggest line.

> According to the [FAO AQUASTAT water use methodology](https://www.fao.org/aquastat/en/overview/methodology/water-use/index.html), 69% of water withdrawn globally goes to agriculture, 12% to municipalities and 19% to industry.

With a line that large, irrigating every field on the same calendar means part of the water and pumping energy goes where it is not needed; we cover tracking flow and pressure in the field in our [irrigation monitoring guide](https://digitalbridge.com.tr/en/blog/irrigation-monitoring-iot.html). Problems missed during the season do not end at harvest either. The [FAO SDG 12.3.1 global food loss indicator](https://www.fao.org/sustainable-development-goals-data-portal/data/indicators/1231-global-food-losses/en/) estimates that 13.3% of food was lost after harvest in 2023 across on-farm, transport, storage, wholesale and processing stages, rising to 25.4% for fruit and vegetables. The field is not the only cause of these losses, but when harvest timing and logistics are not based on realistic area and crop development figures, part of the problem is already set before the crop leaves the field.

The biggest technical obstacle to monitoring crops by satellite is cloud. NASA Earth Observatory's ["Cloudy Earth" feature](https://science.nasa.gov/earth/earth-observatory/cloudy-earth-85843) draws on MODIS observations from NASA's Aqua satellite between 2002 and 2015 to show that about 67% of Earth's surface is typically covered by cloud; over land, only 30% of skies are completely cloud-free. A sound satellite monitoring system is therefore built around a pipeline that discards cloudy pixels and fills gaps from other sources.

## Where does agricultural satellite data come from, and which source should you choose?

The most widely used open source for crop monitoring is the EU's Copernicus programme. Its [data access page](https://www.copernicus.eu/en/access-data) states that, as of 2026, the vast majority of Copernicus data is available to any citizen and organisation worldwide on a free, full and open basis. A farm business in Türkiye can use it just as one in Europe can.

According to [ESA's Sentinel-2 mission page](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2), Sentinel-2 images a 290 km swath in 13 spectral bands at 10 m resolution with a 5-day revisit time. Its red, red-edge and near-infrared bands allow indices such as [NDVI](https://digitalbridge.com.tr/en/glossary.html#ndvi) (Normalised Difference Vegetation Index) and NDRE (Normalised Difference Red Edge index) to be computed directly. [ESA's Sentinel-1 page](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1) describes a radar instrument providing all-weather, day-and-night imagery, which makes it the natural complement when cloud blocks the optical view.

Commercial satellites offer higher resolution and on-demand tasking, at the price of licence fees and usage restrictions. The right source depends on field size and the decision you need to make.

| Data source | Strength | Limitation | Best-fit decision |
|---|---|---|---|
| Open optical satellite (Sentinel-2) | Free, regular, wide area, many bands | 10 m pixels; nothing on cloudy days | Portfolio monitoring, in-season trends, list of problem fields |
| Radar satellite (Sentinel-1) | Unaffected by cloud or darkness | Harder to interpret; does not show plant health directly | Change under cloud, flooding, sowing and harvest tracking |
| Commercial high-resolution satellite | Sub-metre to few-metre detail | Licence cost and terms of use | Small plots, orchards, row-level detail |
| Multispectral drone | Centimetre detail, flies under cloud | Limited area per flight | Detailed map of a flagged field, variable-rate prescriptions |
| In-field sensors and scouting | Real measurement at the root zone, finds the cause | Point data only | Ground truthing, irrigation decisions |

Satellite, radar, drone and in-field sensors are not competitors. The satellite tells you where to look; drones and field sensors tell you why and how much.

## How to monitor crops by satellite: a six-step workflow

1. **Digitise field boundaries.** Monitoring runs per field, so boundaries, crop, sowing date and grower are held as a layer in a geographic information system ([GIS](https://digitalbridge.com.tr/en/glossary.html#gis)). With 10 m pixels, very small or narrow fields suffer from edge effects; flag them from the outset.
2. **Define the decision.** A concrete question such as "which fields are falling behind neighbours with the same crop and sowing window?" sets the index and the alert threshold.
3. **Ingest and clean imagery automatically.** When a new scene arrives, the system clips it to each field and applies cloud and shadow masks. If a field is clouded over, that date is left empty rather than interpolated.
4. **Build a time series.** Index values are stored by date for every field. You read the curve, not a single image: when a field's growth curve departs from the regional average for its crop, it is flagged. This is [anomaly detection](https://digitalbridge.com.tr/en/blog/anomaly-detection-guide.html) applied to farmland.
5. **Prioritise field visits.** Flagged fields land in the field team's mobile list, and visit notes and photos attach to the same field record. Every flag with no cause found is feedback for tuning the threshold.
6. **Drill down when needed.** Where the cause is unclear or the affected area is large, bring in a drone flight, soil moisture data or sampling. The outcome goes into the field's history and the end-of-season report.

### When is satellite not enough?

A 10 m pixel falls short on plots well under a hectare, in vineyards where row detail matters, or when you need a variable-rate fertiliser prescription. Optical satellites such as Sentinel-2 also go blind when cloudy days stack up during a critical growth stage. In those cases, plan for drone flights or commercial high-resolution imagery.

## How do you connect satellite imagery to farm decisions?

If satellite images sit in a folder, decisions are still made by eye. The value comes from keeping imagery alongside the field register, sensor readings and scouting notes. We explain the logic of combining asset layers, multi-date imagery and mobile field updates in our [GIS guide for municipalities](https://digitalbridge.com.tr/en/blog/gis-for-municipalities.html); agriculture uses the same structure.

Imagery, index values and field data pile up quickly over a season. Keeping raw rasters and per-field summary tables in separate tiers speeds up queries and keeps storage costs in check; our [data lake versus data warehouse comparison](https://digitalbridge.com.tr/en/blog/data-lake-vs-data-warehouse.html) sets out the general principle. For management, a handful of indicators is enough: fields monitored, share flagged and time to visit. We cover indicator selection in our [BI dashboard and KPI guide](https://digitalbridge.com.tr/en/blog/bi-dashboard-kpis.html).

Once grower names, phone numbers and field ownership are joined to imagery, the system processes personal data too. In Türkiye this falls under Law No. 6698 on the Protection of Personal Data (KVKK), the country's counterpart to GDPR, so access rights and privacy notices should be designed in from the start.

Satellite monitoring is one layer of a wider farm transformation. The order in which irrigation, greenhouses, herds and field records should go digital is set out in our [farm digital transformation roadmap](https://digitalbridge.com.tr/en/blog/farm-digital-transformation.html). On grazing land, imagery can be read together with GPS collar data; see our [livestock monitoring system](https://digitalbridge.com.tr/en/blog/livestock-monitoring-system.html) article.

## How Digital Bridge delivers satellite imagery analysis for agriculture

We do not resell a satellite subscription. We build a monitoring workflow around your field structure and the decisions you need to make:

- **Discovery and requirements.** Together we map the number of fields, crops, the decisions at stake and how your field team works today. This is where we establish whether open satellite data is enough or whether drone or commercial imagery is needed.
- **Field layer and GIS.** We set up field boundaries, crop and sowing data as layers in a [geographic information system](https://digitalbridge.com.tr/en/data-analytics/geographic-information-systems-gis.html), with satellite and drone imagery as base layers so that the same area can be compared across dates. Field staff add notes and photos from their phones.
- **Pilot.** We start with a limited set of fields over part of a season and measure how often flags are confirmed on the ground. Thresholds and indices are tuned from that feedback.
- **Detail layer.** For flagged fields, our [drone data processing service](https://digitalbridge.com.tr/en/data-analytics/drone-data-processing.html) produces high-resolution NDVI maps, and where relevant we show soil moisture and weather station readings from a [precision agriculture IoT system](https://digitalbridge.com.tr/en/data-analytics/precision-agriculture-iot.html) on the same screen.
- **Integration and reporting.** Field-level indicators reach management through a [BI dashboard](https://digitalbridge.com.tr/en/data-analytics/bi-dashboard.html), and we can connect the system to your existing grower register.

Because sensors, telemetry hardware and software come from the same team, responsibility for the device in the field and the dashboard in the office sits in one place. We compare options for moving sensor data across large estates in [LoRa vs NB-IoT vs cellular](https://digitalbridge.com.tr/en/blog/lora-vs-nb-iot-vs-cellular.html), and cover irrigation in our [soil moisture sensor guide](https://digitalbridge.com.tr/en/blog/precision-agriculture-soil-sensors.html). For our sector approach, see [agriculture and livestock solutions](https://digitalbridge.com.tr/en/industry-solutions/agriculture-livestock.html), and for related topics browse all our [Data & Analytics guides](https://digitalbridge.com.tr/en/blog/topic/veri.html).

## Your next step

Put together a short brief: how many fields you monitor, your crop mix, your field team's weekly visit capacity, and one problem you spotted too late last season. Those four facts are enough to judge how much value satellite monitoring would bring and which fields a pilot should start with. Then [get in touch with us](https://digitalbridge.com.tr/en/contact.html); we work remotely and on site across all of Türkiye's provinces.

## Frequently Asked Questions

### Is satellite imagery for agriculture free?

Open data can be used free of charge. Imagery from the Sentinel satellites of the EU's Copernicus programme is openly available and covers farmland worldwide on a regular cycle. The real cost lies in processing the data, building the field layer, removing cloud and connecting results to the field team's workflow. If you need higher resolution, commercial satellite imagery is licensed separately.

### How often does satellite imagery update a field?

The open Sentinel-2 satellite revisits the same point every 5 days. Because optical images are unusable on cloudy days, the number of usable images for a given field is lower than that. According to NASA, only 30% of skies over land are completely cloud-free. A monitoring system therefore discards cloudy dates and fills the gaps with the next clear image or with weather-independent Sentinel-1 radar data.

### Should I choose satellite or drone?

In most cases you use both. Satellites suit regular, low-cost monitoring of many scattered fields and producing a list of the ones in trouble. Drones deliver centimetre-level detail on a chosen field, can fly under cloud and provide the resolution needed for variable-rate prescriptions. The larger the portfolio, the bigger the satellite's share; the smaller the plot, the more the drone matters.

### How does satellite monitoring cope with cloudy weather?

Optical satellites cannot see through cloud, so cloudy pixels are masked out and that date is left empty for the affected field. The gap is closed by the next clear image or by radar satellite data. Radar works regardless of weather or daylight; it does not show plant health directly, but it helps track changes in crop structure and surface moisture.

### Does satellite imagery work on small fields?

With open satellite data, one pixel covers a 10 m square. On plots well under a hectare, or on long narrow strips, many pixels also include the neighbouring field or a track, and results can mislead. On such plots the satellite only gives a rough trend; for reliable analysis, plan commercial high-resolution imagery or a drone flight instead.

### Does satellite analysis remove the need for field visits?

No. The satellite shows which field behaves differently from its neighbours, but not why. A low index value can come from drought, disease, pests, poor drainage or late sowing. Its real contribution is directing the field team's time to the fields that need it. When visit outcomes are recorded against each field, the system produces sharper flags as the season goes on.

### In what format are results delivered?

Per-field index values and time series are delivered as tables, and maps as raster and vector layers that open in GIS software. The field team sees flagged fields in a mobile list, while management follows indicators such as fields monitored, area flagged and visit status on a dashboard. Export to your existing records system can be scoped as part of the project.