Start your learning journey in
Earth Observation - EO

Never had contact with this subject before? Don't worry, here you will find a basic guide of what you need to know to start using Earth Observation

1. Types of Earth Observation Imagery


In passive imagery systems, sensors are designed to detect electromagnetic emissions from constituents of the Earth's surface and atmosphere. These emissions can be locally produced (e.g. thermal radiation from vegetation in the infrared spectrum) or be the result of reflected sunlight in the visible spectrum. Hence, passive imagery is usually dependent on the day-night cycle and can be degraded or blocked by perturbations coming from unwanted sources of emissions or cloud cover.

Passive Imagery
Figure 1: Atmospheric electromagnetic transparency (Wikipedia)

1.1.1 Panchromatic

Panchromatic images are the result of the measure of light intensity over a broad range of the electromagnetic spectrum. Collecting light from a wide range of wavelengths allows for more energy being collected and hence high resolution images (up to 30 cm in resolution for the best commercially available satellite instruments). A standard example of panchromatic measurement will measure the light intensity coming from the observed scene in the full visible spectrum. This measurement would typically cover wavelengths between 0.47 and 0.83 μm. The resulting product is generally an image displayed as shades of grey, such as presented in Figure 2.

Panchromatic Imagery
Figure 2 (CSCRS)

Another example of panchromatic measurement is done by thermal infrared sensors, at wavelengths between 10 and 12 μm. The intensity of the IR radiation reaching the satellite is directly correlated with the temperature of the object emitting that radiation. Regions where the ground or the ocean is warm will emit the most intense radiation. Because IR is constantly emitted by the Earth and by clouds, it is possible to obtain IR satellite imagery even when the scene is not illuminated by the sun. In contrast, visible satellite imagery which relies on sunlight reflected up to the satellite can only be obtained during the daylight hours.

1.1.2 Multi-spectral

Multi-spectral imagery denotes the remote sensing of an observed scene in several narrow bands of the electromagnetic spectrum. Since the range of wavelengths contributing to the radiation energy detected by the sensor is reduced, multi-spectral instruments will typically have to collect energy on larger spatial extents to “fill” the imaging detector, resulting in a lower resolution than for panchromatic images. A common example of multi-spectral images is the production of “natural color” images by the combination of measurements in 3 bands of the visible spectrum (narrow bands centered around the blue, green and red wavelengths), in the same way as is done in classical consumer cameras. See Figure 3 (left-hand side) for an example of a "natural color" image. Multi-spectral images are not restricted to the visible spectrum: measurements can be done in the infrared (IR) fields, ultraviolet (UV), microwave, etc. Figure 3 (right-hand side) presents an example of a "false color" image, combining the green band (displayed in the blue component of the image), the red band (displayed in the green component of the image) and a near infrared band (displayed in the red component of the image). This visualization combination allows highlighting the presence and health of the vegetation: healthy vegetation creates chlorophyll which reflects near-infrared energy, and therefore appears in darker red on the image.

Multi-spectral Imagery
Figure 3: 3-band multi-spectral imagery (CSCRS)

Many other combinations of wavelength bands are possible, depending on the information to be extracted. For example:

  • Shortwave infrared (red), near infrared (green), and green (blue): often used to show floods or newly burned land
  • Blue (red), two different shortwave infrared bands (green and blue): used to differentiate between snow, ice, and clouds
  • Blue (blue), near infrared (green), mid infrared (red): used to picture on one image water depth, vegetation coverage, soil moisture content, and the presence of fires

1.1.3 Pan-sharpened

Pan-sharpening is a numerical process that merges multi-spectral images with panchromatic images to provide high resolution coloured images. This technique is useful to perform image analysis combining the spectral resolution of multi-spectral images with the improved spatial resolution of panchromatic images. This is illustrated in Figure 4.

Figure 4: Example of Pan-Sharpening (CSCRS)

1.1.4 Hyper-spectral

Hyper Spectral
Figure 5: Example of a Hyperspectral Data Product (HySpex)

Hyperspectral imagery aims at obtaining a nearly-continuous spectrum for each pixel in the image of a scene, extending the benefits of multi-spectral imagery, which measures light intensity on a limited number of separate bands of the electromagnetic spectrum. Figure 5 provides an example of representation of a hyperspectral data product, each layer of the cube picturing the same 2D scene observed in one specific wavelength λ. For each pixel, a hyperspectral sensor acquires the light intensity for a large number (typically a few tens to several hundred) of contiguous narrow spectral bands. To every pixel in the image is thus attached a nearly continuous spectrum. The high spectral resolution of a hyperspectral imager allows for detection, identification and quantification of surface materials, as well as inferring biological and chemical processes. Hyperspectral Earth Observation is for now mainly limited to aerial imagery and scientific demonstration missions.

2.1.5 Microwave Radiometry The main objective of the Microwave Radiometer (MWR) is the measurement of the integrated atmospheric water vapor column and cloud liquid water content, as correction terms for the radar altimeter signal (see Section 2.2.3 Radar Altimetry). In addition, MWR measurement data are useful for the determination of surface emissivity and soil moisture over land, for surface energy budget investigations to support atmospheric studies, and for ice characterisation.


In active imagery systems, instruments are composed of a transmitter that sends out a specific electromagnetic signal and of a sensor receiving the interaction of the signal with the Earth’s surface. Such observations are not dependent on solar illumination.

2.2.1 Synthetic Aperture Radar

The most common active sensor used for Earth Observation is the Synthetic Aperture Radar (SAR). This instrument transmits electromagnetic pulses towards the Earth’s surface where they are reflected or scattered by the surface features. The instrument’s antenna can detect and record the return pulses. The intensity of the return pulse and the time it takes to arrive back at the antenna are used to generate SAR imagery. The main advantage of radar imaging is that it is insensitive to the day/night cycle and most of the time to the meteorological conditions (shorter wavelength signals such as X-band can be degraded by heavy intense rain cells). The selected radio band impacts what is observed from the scene by influencing the level at which the incident radiation will backscatter. Applications include (for instance) ship detection, oil spill detection, sea ice monitoring (see Figure 6), forest monitoring, soil moisture, critical infrastructure, etc.

Syntetic Aperture Radar
Figure 6: Example of SAR imagery for monitoring formation of Icebergs (ESA ENVISAT)

By using a technique known as SAR interferometry, highly accurate measurements of geophysical parameters such as surface topography, ground deformation and subsidence and glacier movements can be made. In SAR interferometry, the phase of two or more complex radar images are compared that have been acquired from slightly different positions or at different times. Since the phase of each SAR image pixel contains range information that is accurate to a small fraction of the radar wavelength, it is possible to detect and measure path length differences with centimetric or even millimetric precision. With across-track interferometry the radar images are acquired from mutually displaced flight tracks , enabling (for instance) a precise measurement of the surface topography. By using an external DEM, the topographic information can be subtracted from the interferogram, leading to a differential SAR interferometric measurement where subtle (mm) changes of the range distance between the two acquisitions (e.g. due to subsidence) can be detected. Further potential is possible by comparison of the coherence between several data acquisitions, which can be used for land classification and change detection. With along-track interferometry, the radar images are acquired from one and the same flight track but at different times, enabling (for instance) the observation of ocean surface currents.

2.2.2 Lidar

Lidar (Light Detection And Ranging) EO uses the same principle as SAR but works in the IR, visible or UV wavelengths. Lidars are used for precise measurement of topographic features, monitoring growth or decline of glaciers, profiling clouds, measuring winds, studying aerosols and quantifying various atmospheric components. The Atmospheric Lidar ATLID on ESA’s EarthCare mission will provide vertical profiles of aerosols and thin clouds. It operates at a wavelength of 355 nm and has a high-spectral resolution receiver and depolarisation channel.

More information on ATLID can be found at ESA.

The Atmospheric Laser Doppler Lidar Instrument ALADIN on ESA’s Aeolus-ADM mission will measure Line-of-Sight wind profiles at different levels in the atmosphere from the troposphere to the lower stratosphere with vertical resolution of 250 m - 2 km. It operates at a wavelength of 355 nm, with spectrometers for molecular Rayleigh and aerosol/cloud Mie backscatter. ALADIN will be the first wind lidar in space to obtain aerosol/cloud optical properties (backscatter and extinction coefficients).

2.2.3 Radar Altimetry

Radar altimeters are active sensors that use the ranging capability of radar to measure the surface topography profile along the satellite track. They provide precise measurements of a satellite's height above the ocean by measuring the time interval between the transmission and reception of very short electromagnetic pulses. A variety of parameters may be inferred using the information from radar altimeter measurements, such as time-varying sea-surface height (ocean topography), the lateral extent of sea ice and altitude of large icebergs above sea level, as well as the topography of land and ice sheets, and even that of the sea floor. Satellite altimetry also provides information for mapping sea-surface wind speeds and significant wave heights. Jason-3 and Jason-CS (Sentinel 6) are contributing radar altimetry missions of the Copernicus programme, which will provide the continuity of critical high precision observations of ocean surface topography until 2030+, in full synergy with the marine mission of the Copernicus Sentinel 3.

Radar Altimetry
Figure 7: Altimetry-derived mean dynamic topography (ESA)

2.2.4 GNSS-R

GNSS reflectometry (GNSS-R) is a relatively new category of satellite navigation applications which entails a method of remote sensing to receive and process microwave signals reflected from various surfaces to extract information about those surfaces. In this process, the GNSS satellite acts as the transmitter and an airplane or Low Earth Orbit (LEO) satellite as the receiving platform. For altimetry applications, a GNSS-R receiver can also be placed on the land. An advantage of GNSS-R remote sensing is the ubiquity of signal sources, including GPS, Galileo, GLONASS, and Beidou/Compass. A wide range of applications is possible such as wide-swath altimetry, sea-wind retrieval, and measurement of seawater salinity and ice-layer density, as well as humidity measurements over land.

Figure 8: Starlab GNSS-R Sensor Oceanpal for monitoring lake level in ESA Business Applications project INTOGENERContent taken from Newcomers Earth Observation Guide

How Earth Observation can help in different scenarios - some examples


Agriculture plays a dominant role in economies of both developed and undeveloped countries. Whether agriculture represents a substantial trading industry for an economically strong country or simply sustenance for a hungry, overpopulated one, it plays a significant role in almost every nation. The production of food is important to everyone and producing food in a cost-effective manner is the goal of every farmer, large-scale farm manager and regional agricultural agency. A farmer needs to be informed to be efficient, and that includes having the knowledge and information products to forge a viable strategy for farming operations. These tools will help him understand the health of his crop, extent of infestation or stress damage, or potential yield and soil conditions. Commodity brokers are also very interested in how well farms are producing, as yield (both quantity and quality) estimates for all products control price and worldwide trading.


Monitoring the health of forests is crucial for sustainability and conservation issues. Depletion of key species such as mangrove in environmentally sensitive coastline areas, removal of key support or shade trees from a potential crop tree, or disappearance of a large biota acting as a CO2 reservoir all affect humans and society in a negative way, and more effort is being made to monitor and enforce regulations and plans to protect these areas.
International and domestic forestry applications where remote sensing can be utilized include sustainable development, biodiversity, land title and tenure (cadastre), monitoring deforestation, reforestation monitoring and managing, commercial logging operations, shoreline and watershed protection, biophysical monitoring (wildlife habitat assessment), and other environmental concerns.

Land Cover / Biomass Mapping

Regional land cover mapping is performed by almost anyone who is interested in obtaining an inventory of land resources, to be used as a baseline map for future monitoring and land management. Programs are conducted around the world to observe regional crop conditions as well as investigating climatic change on a regional level through biome monitoring. Biomass mapping provides quantifiable estimates of vegetation cover, and biophysical information such as leaf area index (LAI), net primary productivity (NPP) and total biomass accumulations (TBA) measurements - important parameters for measuring the health of our forests, for example.

Content taken from Remote Sensing Tutorials