20 Space Acronyms That Help Farmers

 20 Space Acronyms That Help Farmers

Featured Image Credit: Bioscope

Agriculture is one of the key application areas of satellite data. We have all heard about precision farming using satellite navigation signals and a large array of Earth Observation techniques that help farmers identify soil characteristics and crop health. In this article we describe these techniques in a list of space data ‘indices’ that describe soil and crop traits that help farmers make decisions on what to plant, where and when.

Sensors and Wavelengths

Earth Observation satellites carry many different sensors that measure characteristics of the Earth below. We all know the optical images in Google Earth, using sensors similar to those in our phones or photo cameras. However, these cameras only capture a very small part of the electromagnetic spectrum, so satellites also ‘see’ in spectra invisible to the human eye, like infrared, ultraviolet and even microwave and X-ray wavelengths. Additionally there are satellites that use synthetic aperture radar (SAR) to send and receive reflected radar waves from the surface of the planet.

When speaking of Earth Observation data we talk about all these different wavelengths, each measuring different things. In the optical spectrum, plants, for example, reflect mostly green light back from the sun, while absorbing the other colours/wavelengths. Similarly, plants reflect and absorb other wavelengths too, telling us not just the colour of the leaves, but many other important characteristics of the plant.

Satellite applications use all these different sensors and wavelengths, telling us about the health of things they see. And where single wavelengths tell us stories about particular characteristics of , again for example plants, mixing these wavelengths reveals even more information. This information is critically important for farmers, and satellites are often the best source of this important information.

Especially for agriculture, the space data industry has developed a set of indicators that help farmers make decisions on soil and crop health, helping them make daily decisions that increase crop yield, improve health, decrease the need for pesticides, decrease water use for irrigation and increase efficiency and income.

Space data indicators for farmers

In this list we have identified 20 Earth Observation space data indicators that are relevant to farmers.

NDVI: The Normalized Difference Vegetation Index identifies the strength and the vitality of the vegetation on the earth’s surface. It describes the difference between visible and near-infrared reflectance of vegetation cover and can be used to estimate the density of green on an area of land. This is the most common vegetation indicator and can be found readily available in most Earth Observation tools, like the Sentinel Hub EO Browser.

Sentinel-2 NDVI over Midi-Pyrenees (image: ESA)

NDRE: Normalized Difference Red Edge index is a method of measuring the amount of chlorophyll in the plants. The best timing to apply NDRE is mid-to-late growing season when the plants are mature and ready to be harvested. At this point, other indices would be less effective to use.

GNDVI: The Green Normalized Difference Vegetation Index identifies different concentration rates of chlorophyll, correlated at nitrogen.

TNDVI: The Transformed Normalized Difference Vegetation Index indicates a relation between the amount of green biomass that is found in a pixel, correlated with the spatial resolution of the image.

GEMI: The Global Environmental Monitoring Index permits the comparison of vegetation index values for different dates, without the requirement of atmospherically corrected data.

DVI: The Difference Vegetation Index is sensitive to the amount of vegetation in a location (in a pixel). The similar PVI (Perpendicular Vegetation Index) is a generalisation of the DVI, which allows for soil lines of different slopes. And the WDVI (Weighted Difference Vegetation Index) is related to the PVI index.

RVI: The Ratio Vegetation Index is the simplest ratio-based indicator, providing information on the height and amount of vegetation.

IPVI: The Infrared Percentage Vegetation Index is equivalent to NDVI and RVI, but is faster to calculate.

ARVI:  The Atmospherically Resistant Vegetation Index takes advantage of the different scattering responses from blue and red wavelengths to retrieve information regarding the atmosphere opacity. This index has a similar dynamic range to the NDVI, but is, on average, four times less sensitive to atmospheric effects than the NDVI.

NDI45: The Normalized Difference Index is more linear, with less saturation at higher values than the NDVI.

MTCI: The Meris Terrestrial Chlorophyll Index is useful for observing the chlorophyll contents, vegetation senescence, and stress for water and nutritional deficiencies, but it is less suitable for land classification.

MCARI: The Modified Chlorophyll Absorption Ratio Index is responsive to leaf chlorophyll concentration and ground reflectance.

REIP: The Red Edge Inflection Point Index includes the information of chlorophyll content and growth status.

S2REP: The Sentinel 2 Red Edge Position Index includes the information of chlorophyll content and growth status.

IRECI: The Inverted Red Edge Chlorophyll Index is used as an indicator of stress and senescence of vegetation.

PSSRa: The Pigment Specific Simple Ratio (chlorophyll index) quantifies pigments at the scale of the whole plant canopy.

SAVI: The Soil Adjusted Vegetation Index includes information about vegetation and grass canopies.

TSAVI: The Transformed Soil Adjusted Vegetation Index assumes that the soil line has arbitrary slope and intercept, and it makes use of these values to adjust the vegetation index.

MSAVI: The Modified Soil Adjusted Vegetation Index is an index designed to substitute NDVI and NDRE where they fail to provide accurate data due to low vegetation or a lack of chlorophyll in the plants. works where other vegetation indices do not – during seed germination and leaf development stages. You can use MSAVI to monitor seedlings when there is a lot of bare soil in the field.

The use of space data in agriculture has become the tool necessary to the monitoring of the growth of crops in the agriculture, with the aims to improve production and reduce the cost involved. This tool support to the farmers to determine the best inputs needed in each area.

Where to find these indices?

Several of these indices, especially the very common NDVI, are available in most Earth Observation platforms, as you can see in below example from the Sentinel Hub EO Browser.

Sentinel-2 NDVI over Ukraine. Image created from EO Browser (author)

Find a comparison between EO Browser and Nimbo Maps here.

In the new version of the ESA Sentinel Applications Platform SNAP 9.0 many of the mentioned indexes that support farmers and forestry managers are included. SNAP is less intuitive to use than the EO Browser and NIMBO Maps, but has a much wider variety of indices and other tools to get exactly the information you need for your specific application.

SNAP is the tool of choice when looking to monitoring cultivated areas, obtaining information about the status of growth, strength, rates of chlorophyll, plant vitality, nitrogen content, senescence, and many other vegetation characteristics.


ESA has developed free open source toolboxes for the scientific exploitation of Earth Observation missions. STEP is the ESA community platform for accessing the software and its documentation, communicating with the developers, dialoguing within the science community, promoting results and achievements as well as providing tutorials and material for training scientists using the Toolboxes.

The ESA toolboxes support the scientific exploitation for the ERS-ENVISAT missions, the Sentinels 1/2/3 missions and a range of National and Third Party missions. The three toolboxes are called respectively Sentinel 1, 2 and 3 Toolboxes and share a common architecture called SNAP.

In June 2022 the latest SNAP 9.0 version was released, containing new functionality, improvements and bug fixes. Find all info about SNAP 9.0 here.

Gabriela Quintana Sánchez

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