# Crop monitoring and precision farming with satellite data Satellites help farmers in two ways. Earth observation shows how the crop develops across every field, for example through vegetation indices from Sentinel-2 images taken every few days. Satellite positioning steers machines and places fertiliser, seed and water where the field needs them. Combined, the two make variable-rate application possible. ## How it works Crop monitoring reads the state of the plants from space. Precision farming uses that reading to decide how much each part of a field receives. **Earth observation** measures how strongly the crop reflects red and near-infrared light. Vegetation indices such as the NDVI turn these two bands into a value for greenness, which helps to understand vegetation density and to assess changes in plant health [1]. Sentinel-2 adds four red-edge bands designed for vegetation [2]. According to EUSPA, Earth observation lets a farm follow crop performance without visiting every field and use less fertiliser and other inputs [6]. ESA names crop health, disease detection, yield prediction, soil moisture and irrigation among the uses of Earth observation in farming [8]. **Satellite positioning (GNSS)** is what turns such a map into action in the field. Variable-rate application combines GNSS positioning with Earth observation to distribute different amounts of seed and agrichemicals across a field, and precision irrigation does the same with water: the image shows where the crop needs more or less, and the receiver on the machine knows where it is [6]. EUSPA describes the aim as using less fuel, water and chemicals [7]. **Figure:** Illustrative map, not real data. Field parcels coloured by crop vigour, from low (light) to high (dark), as a vegetation index layer shows them. Sensors in the field can add measurements that satellites cannot take from orbit. Where no mobile network reaches them, they can send their data by satellite, as described in the use case on [satellite IoT](https://leto.space/use-cases/satellite-iot/). ## Which data and satellites | Question | Data | Example | | ------------------------------------------ | ---------------------------------------- | -------------------------------- | | How is the crop developing in each field? | Optical images and vegetation indices | Copernicus Sentinel-2 | | What happens under a closed cloud cover? | Radar images | Copernicus Sentinel-1 | | Where does the machine apply more or less? | GNSS positioning plus an application map | Galileo and EGNOS on the machine | | Where exactly are the field boundaries? | Earth observation and GNSS | Field mapping services | Satellite data is already part of farm policy in the EU. Under Regulation (EU) 2021/2116 every member state runs an area monitoring system, defined as the regular and systematic observation of agricultural activities and practices with Copernicus Sentinel data or data of at least equivalent value. Since 1 January 2024 this system has had to be fully operational in all member states [9]. ## What you get - **Crop vigour maps** for each field, updated whenever a clear image is available [2]. - **Zones within a field** that develop differently, as the basis for variable-rate application of fertiliser, seed and agrichemicals [6]. - **Signs of stress or disease**, which tell you which fields to walk first [8]. - **Yield estimates** before harvest [8]. - **Irrigation support** from soil moisture data [8]. ## Limits **Limits** - **Cloud gaps:** Optical satellites cannot see through cloud, while radar can \[4]. Cloudy weeks can leave gaps exactly when a decision is due. - **Pixel size:** At 10 m, one Sentinel-2 pixel covers 100 square metres \[2]. Narrow strips, field edges and small plots mix with their surroundings, and single plants are not visible. - **Greenness is not a diagnosis:** A lower index shows that the crop differs from the rest of the field, not why. Water, nutrients, disease or a sowing problem can all cause it, so the signal still needs a look in the field. ## How we help Before you choose a crop monitoring service, three questions need an answer: which data fits your crops and field sizes, how often you need it, and how it reaches your farm software and machines. In a [strategy engagement](https://leto.space/consulting/strategy/) we answer these questions with you, and we then write the requirements for choosing a provider. [More on requirements and procurement](https://leto.space/consulting/procurement/) To see who offers such services, use [Spacegraph](https://leto.space/spacegraph/), our discovery platform. Its marketplace has a use-case category for vegetation and crop health. ## Related use cases - [Monitoring forest health and deforestation](https://leto.space/use-cases/forest-monitoring/): The same imagery, applied to forest damage and clearings. - [Connecting remote sensors via satellite IoT](https://leto.space/use-cases/satellite-iot/): Field sensors that report where there is no mobile network. - [Agriculture, forestry and environment](https://leto.space/industries/agriculture-forestry-and-environment/): All use cases for farms, forests and environmental bodies. ## Frequently asked questions ### Which satellite data is used for crop monitoring, and how often is it updated? The main free source is Sentinel-2: it records 13 spectral bands, four of them at 10 m, and with two satellites revisits the same place every five days at the equator [2]. Like all Copernicus data it is free of charge [5]. Cloud can hide a field on a given pass, so the usable interval is often longer. Radar satellites such as Sentinel-1 deliver images in any weather, day and night [3]. ### Which satellite positioning do farm machines use? In Europe, 97% of new tractors that use GNSS are equipped with EGNOS, and 69% of new agricultural machinery is equipped with Galileo, according to EUSPA. EUSPA also writes that Earth observation data, "combined with Galileo's centimetre accuracy and boosted by EGNOS, can enable accurate and efficient variable rate applications" [7]. ### How do we start a pilot for crop monitoring? Pick a few fields and one decision you want to improve, for example the second nitrogen application or the irrigation schedule. Then check which data your farm management software and machine terminals can already take in. ## Sources 1. [Landsat Normalized Difference Vegetation Index, U.S. Geological Survey (USGS), accessed 2026-10-06](https://www.usgs.gov/landsat-missions/landsat-normalized-difference-vegetation-index) 2. [Sentinel-2 mission, European Space Agency (ESA), SentiWiki, accessed 2026-10-06](https://sentiwiki.copernicus.eu/web/s2-mission) 3. [Sentinel-1, European Space Agency (ESA), accessed 2026-10-06](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1) 4. [Sentinel-1 mission… did you know?, European Space Agency (ESA), 2025-11-03](https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1/Sentinel-1_mission_did_you_know) 5. [Copernicus, European Commission, DG Defence Industry and Space (EU Space portal), accessed 2026-10-07](https://eu-space.europa.eu/earth-observation/copernicus) 6. [EU Space Market Report 2026, Issue 1, EU Agency for the Space Programme (EUSPA), 2026-05-26](https://www.euspa.europa.eu/sites/default/files/documents/EU_Space_Market_Report_2026.pdf) 7. [Agriculture (EU Space Programme market segment page), EU Agency for the Space Programme (EUSPA), accessed 2026-10-06](https://www.euspa.europa.eu/industry-sectors/agriculture) 8. [ESA Space Solutions: Space for the bioeconomy, European Space Agency (ESA), ESA Space Solutions, accessed 2026-10-06](https://business.esa.int/bioeconomy) 9. [Regulation (EU) 2021/2116 of 2 December 2021 on the financing, management and monitoring of the common agricultural policy, European Union, EUR-Lex, 2021-12-06](https://eur-lex.europa.eu/eli/reg/2021/2116/oj/eng) Page: