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Use cases

Agriculture monitoring

Satellite imagery is revolutionising agriculture and can help European farmers and public authorities take land monitoring to a new level. Sentinel-2 data offers 10-20m spatial resolution, 5-day revisit frequency, global coverage and compatibility to the Landsat missions and provides new opportunities for regional to global agriculture monitoring. Monitoring of crop conditions, soil properties and mapping tillage activities, help to assess land use, predict harvests, monitor seasonal changes and assist in implementing policy for sustainable development. SENTINEL data can also be used for monitoring the changes of agricultural production and productivity of pastures caused by drought and monitoring the decline of land productivity and soil degradation due to excessive cultivation and pasturage and improper irrigation.

Agricultural maps enable provision of independent and objective estimates of the extent of cultivation in a given country or growing season, which can be used to support efforts to ensure food security in vulnerable areas.

Browse through images to see the example of annual vegetation development.


The NDVI provides quantitative information on the health of the crop in the field. The green colored zones have the most robust and volume of vegetation while the yellow and red zones represent less vegetation. This information can be used to make management decisions on the application of inputs like fertilizer and fungicide.

Land change detection

Sentinel satellites provide support to land monitoring services and will ensure frequent and systematic coverage to support the mapping of land cover, classification and change maps, and accurate assessment of geophysical parameters.

With land change maps it is easy to identify developments in the land - e.g. new structures being built, deforestation happening, etc. As different types of land (e.g. arable land, forest, pastures, urban areas) have different patterns of index change (e.g. EVI, NDVI, etc.). By observing greenness time series patterns it is possible to detect changes. Operators can then analyse the results closely by observing original images and decide on required action

Land change maps

Satellite image data is highly useful for creating or updating base maps and detecting major changes in urban land cover and land use.

Satellite imagery provides support to land monitoring services and ensure frequent and systematic coverage to support the mapping of:

  • Land cover development
  • Classification and change maps
  • Environmental analysis
  • Irrigated landscape mapping
  • Carbon Storage and Avoidance
  • Yield determination
  • Soils and Fertility Analysis
  • And other accurate assessment of geophysical parameters

Drought monitoring

Drought is a complex natural phenomenon, and its impacts on agriculture are enormous. Early detection of droughts is important for managing emerging crop losses to prevent or mitigate possible related famines, and for dealing with increased fire risk. Satellite imagery helps to monitor precipitation, soil moisture, and vegetation health to support drought early warning systems. It is used to feed monthly drought bulletins and to issue warnings.

Satellite imagery can assist environmental monitoring by detecting changes in the Earth's vegetation, atmospheric trace gas content, sea state, ocean color, and ice fields. By monitoring vegetation changes over time, droughts can be monitored by comparing the current vegetation state to its long term average.

How are droughts monitored from Space?

Meteorological droughts are defined by rainfall deficiency over an extended period of time and can turn into agricultural droughts, which are characterized by a soil water deficiency and subsequent plant water stress and reduced yield. Agricultural droughts can then turn into hydrological droughts, which refer to deficiencies in surface and subsurface water supplies. The different drought definitions imply that several parameters are used to monitor drought: precipitation, temperature, evapotranspiration, soil moisture, and vegetation. These parameters can be observed from space. Using vegetation indices is a popular approach due to its simplicity. Vegetation indices like NDVI and EVI are freely available in near-real-time (cf. Data Application of the Month on vegetation indices). In addition, calculating anomalies or the Vegetation Condition Index indicating the state of the greenness of vegetation in relation to a time-series is straight-forward

In the video below, see the example of drought monitoring in Slovenia
(study made by: Space.si and ZRC SAZU )

Insurance industry

Earth-observing satellites can map natural phenomena such as floods and earthquakes, track hurricanes and monitor land subsidence across the globe. This information can be valuable to insurance companies for risk and damage assessment. Information obtained by satellite observations can save money and make the insurance industry more efficient.

Assesing floods

Accurate and timely information about a flood’s extent can help insurers to assess the impact and prepare to meet the claims.

Flood extent map

Due to heavy rainfall in September, 2010 large areas in Slovenia were affected by floods. The most affected areas were the capital Ljubljana and its surroundings. Initial damage was estimated to reach €15 million. Animation shows how the floods retreated.

Assessment of damage of floods currently requires field visit, costs a lot of money, takes time and is unreliable due to lack of information.

Using multi-temporal and multi-sensor satellite data it is possible to quickly asses the situation prior to the event, immediately after the event and also monitor further activity a few weeks after the event.

Process workflow

Let’s take a step-by-step look at how a typical (e.g. hail damage) assessment workflow would look like.

  • Importing input data to the GIS in the cloud
    • Parcel boundaries (either SHP files or manual digitization with GIS tools)
    • Claim data (crop type, state of development, damage)
    • Markation of samples for damaged and non-damaged parts of the land (manually with GIS)
  • Automatic calculation of vegetation indices and imagery previews
    • True colour
    • False colour
    • NDVI (normalized differential vegetation index)
    • SAVI (Soil adjusted vegetation index)
    • LAI (Leaf area index)
  • Adjustment of the models to local conditions
    • Performed on sample part of the fields
  • Automatic calculation of the damage assessment data
    • State of development of the plant
      • Based on comparison of LAI on non-damaged parts with standard LAI for claimed crop.
    • Defolation of crops in damaged areas based on LAI
    • Assessment of damaged areas
      • Division of parcel to areas based on percentage of damage (100%, 90%,..)
  • Manual adjustment of the results using GIS tools and visual interpretation of all available data
    • Comparison with HR Sentinel/Landsat imagery archive can help with assessing development of the crops in the past
  • Production of repots for each parcel (in XLS)
    • Comparison between claimed/observed data
      • Total area of the parcel
      • Total area damaged
      • State of development
      • Defolation of the plant