Satellite imagery

Satellites are one of the most used means in agriculture to perform remote sensing. The satellite imagery in fact allows to monitor crops remotely in a precise and efficient way.  

Spatial and temporal resolution of satellites

There are many satellites that acquire multispectral images from space: the most common are Sentinel-2and Landsat 8 (both used in Agricolus platform), Planetscope and Sky Sat. The images obtained have a spatial resolution of a few meters: Landsat 8 provides data with a spatial resolution of 30 m, while Sentinel-2 of 10, 20 or 60 m (depending on the band), Planetscope of 3 m and SkySat of 1 m.

The temporal resolution is in most cases regular. For example, Landsat 8 is available every 16 days, while Sentinel-2 is available every 3/5 days (depending on the zone). Planetscope and Skysat have a daily resolution.

The regular passage of the satellites determines the availability of the data in several phases of the growing season, but it is also important to underline that during the satellite transit, where the area under examination is covered by clouds, the data is not usable.

Data processing

Thanks to a sophisticated proprietary technological structure, Agricolus is able to manage several services and process satellite images autonomously.

Through the Copernicus Open Access Hub portal, Agricolus has access to Sentinel-2 satellite data and integrates them to provide vegetation indices to end users. The integration of satellite imagery is done through the DIAS (Copernicus Data and Information Access Service) service of the ONDA consortium, initiated by the European Commission, and ESA.

Satellite imagery is then cropped to field boundaries with the support of L3Harris Geospatial technologies.

Finally, Sentinel-2 bands are processed by Agricolus to calculate multiple indices of vigor, water stress and chlorophyll. Indices are provided for all available dates, automatically excluding images with cloud cover.

Vegetation indices obtained by satellite

When talking about satellite images, and remote sensing in general, it is necessary to introduce the concept of vegetation index to understand how they allow the monitoring of the health of crops without the need to go to the field.

Vegetation indexes are a key tool of Smart Farming: the use of satellite data and their correct interpretation reduce the interventions in the field and make sustainable, from the economic point of view, a structured crop scouting activity.

The indices can describe the vigor of the plant, providing a measure of its general health, or specific problems such as water stress or the amount of chlorophyll.

Types of vegetation indexes

NDVI: it allows to evaluate the health of the vegetation, analyzing the reflectance of the vegetation in the Red and NIR bands.

GNDVI (Green-NDVI): it provides an indication of the health of the vegetation and reduces the saturation effect when the vegetation is particularly developed.

WDRVI: it analyzes the health of the vegetation and is particularly useful when the vegetation is well developed and lush and the other vegetation indices tend to saturate.

SAVI: allows to evaluate the conditions of vegetation development in the emergency and early stages of development, as it applies a correction to bare soil.

LAI: leaf area index that estimates the leaf area of the plant expressed in m2 on m2.

TCARI/OSAVI: specific index that allows to identify chlorotic areas within the field.

NDMI: specific index that evaluates the water content of the vegetation, therefore usable only with developed vegetation.

NMDI: can be used to assess the water content of the soil; in case of bare soil, a high index value indicates dry soil. In the presence of vegetation, a high index value indicates that the plant is not under water stress.

Historical and comparative data

In agriculture it is essential to monitor and compare crop development on different plots and during different years.

The four main steps for the interpretation of vegetation indices are in fact the multi-temporal analysis and the comparison between indices: 1) the assessment of the phenological stage of the plant; 2) the analysis of the historical trend of indices to assess whether there are anomalies and whether they are related to known phenomena; 3) the identification of indices to be compared; 4) the comparison between indices to identify critical areas to be verified in the field.

The comparison of satellite images allows us to evaluate the relationships between different indices (such as low vigor and high water stress) and the possible causes of their variations.

The graph of the historical trend of vegetation indices also allows to keep track of what happens in the field and to evaluate the changes compared to the data of previous years.

Do you want to know how we use these technologies in the Agricolus platform?

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