Brief Introduction to Remote Sensing (1/3): Basic Definitions

I am still working on the new tutorials about the Semi-Automatic Classification Plugin. In the meantime, I think it is useful to write some posts about remote sensing basics that are already in the user manual of the plugin. 

GIS definition

There are several definitions of GIS (Geographic Information Systems), which is not simply a program. In general, GIS are systems that allow for the use of geographic information (data have spatial coordinates). In particular, GIS allow for the view, query, calculation and analysis of spatial data, which are mainly distinguished in raster or vector data structures. Vector is made of objects that can be points, lines or polygons, and each object can have one ore more attribute values; a raster is a grid (or image) where each cell has an attribute value (Fisher and Unwin, 2005). Several GIS applications use raster images that are derived from remote sensing.

Remote Sensing definition

A general definition of Remote Sensing is “the science and technology by which the characteristics of objects of interest can be identified, measured or analyzed the characteristics without direct contact” (JARS, 1993).
Usually, remote sensing is the measurement of the energy that is emanated from the Earth’s surface. If the source of the measured energy is the sun, then it is called passive remote sensing, and the result of this measurement can be a digital image (Richards and Jia, 2006). If the measured energy is not emitted by the Sun but from the sensor platform then it is defined as active remote sensing, such as radar sensors which work in the microwave range (Richards and Jia, 2006).
The electromagnetic spectrum is “the system that classifies, according to wavelength, all energy (from short cosmic to long radio) that moves, harmonically, at the constant velocity of light” (NASA, 2013). Passive sensors measure energy from the optical regions of the electromagnetic spectrum: visible, near infrared (i.e. IR), short-wave IR, and thermal IR (see Figure Electromagnetic-Spectrum).
by Victor Blacus (SVG version of File:Electromagnetic-Spectrum.png)
[CC-BY-SA-3.0 (]
via Wikimedia Commons

The interaction between solar energy and materials depends on the wavelength; solar energy goes from the Sun to the Earth and then to the sensor. Along this path, solar energy is (NASA, 2013):
  • Transmitted - The energy passes through with a change in velocity as determined by the index of refraction for the two media in question.
  • Absorbed - The energy is given up to the object through electron or molecular reactions.
  • Reflected - The energy is returned unchanged with the angle of incidence equal to the angle of reflection. Reflectance is the ratio of reflected energy to that incident on a body. The wavelength reflected (not absorbed) determines the color of an object.
  • Scattered - The direction of energy propagation is randomly changed. Rayleigh and Mie scatter are the two most important types of scatter in the atmosphere.
  • Emitted - Actually, the energy is first absorbed, then re-emitted, usually at longer wavelengths. The object heats up.


Sensors can be on board of airplanes or on board of satellites, measuring the electromagnetic radiation at specific ranges (usually called bands). As a result, the measures are quantized and converted into a digital image, where each picture elements (i.e. pixel) has a discrete value in units of Digital Number (DN) (NASA, 2013). The resulting images have different characteristics (resolutions) depending on the sensor. There are several kinds of resolutions:
  • Spatial resolution, usually measured in pixel size, “is the resolving power of an instrument needed for the discrimination of features and is based on detector size, focal length, and sensor altitude” (NASA, 2013); spatial resolution is also referred to as geometric resolution or IFOV;
  • Spectral resolution, is the number and location in the electromagnetic spectrum (defined by two wavelengths) of the spectral bands (NASA, 2013) in multispectral sensors, for each band corresponds an image;
  • Radiometric resolution, usually measured in bits (binary digits), is the range of available brightness values, which in the image correspond to the maximum range of DNs; for example an image with 8 bit resolution has 256 levels of brightness (Richards and Jia, 2006);
  • For satellites sensors, there is also the temporal resolution, which is the time required for revisiting the same area of the Earth (NASA, 2013).

Radiance and Reflectance

Sensors measure the radiance, which corresponds to the brightness in a given direction toward the sensor; it useful to define also the reflectance as the ratio of reflected versus total power energy.

Spectral Signature

The spectral signature is the reflectance as a function of wavelength (see Figure Spectral Reflectance Curves of Four Different Targets); each material has a unique signature, therefore it can be used for material classification (NASA, 2013).
Spectral Reflectance Curves of Four Different Targets
(from NASA, 2013)

Landsat Satellite

Landsat is a set of multispectral satellites developed by the NASA (National Aeronautics and Space Administration of USA), since the early 1970’s.
Landsat images are very used for environmental research. The resolutions of Landsat 4 and Landsat 5 sensors are reported in the following table (from; also, Landsat temporal resolution is 16 days (NASA, 2013).
Landsat 4, Landsat 5 BandsWavelength [micrometers]Resolution [meters]
Band 1 - Blue0.45 - 0.5230
Band 2 - Green0.52 - 0.6030
Band 3 - Red0.63 - 0.6930
Band 4 - Near Infrared (NIR)0.76 - 0.9030
Band 5 - SWIR1.55 - 1.7530
Band 6 - Thermal Infrared10.40 - 12.50120 (resampled to 30)
Band 7 - SWIR2.08 - 2.3530

The resolutions of Landsat 7 sensor are reported in the following table (from; also, Landsat temporal resolution is 16 days (NASA, 2013).
Landsat 7 BandsWavelength [micrometers]Resolution [meters]
Band 1 - Blue0.45 - 0.5230
Band 2 - Green0.52 - 0.6030
Band 3 - Red0.63 - 0.6930
Band 4 - Near Infrared (NIR)0.77 - 0.9030
Band 5 - SWIR1.57 - 1.7530
Band 6 - Thermal Infrared10.40 - 12.5060 (resampled to 30)
Band 7 - SWIR2.09 - 2.3530
Band 8 - Panchromatic0.52 - 0.9015

The resolutions of Landsat 8 sensor are reported in the following table (from; also, Landsat temporal resolution is 16 days (NASA, 2013).
Landsat 8 BandsWavelength [micrometers]Resolution [meters]
Band 1 - Coastal aerosol0.43 - 0.4530
Band 2 - Blue0.45 - 0.5130
Band 3 - Green0.53 - 0.5930
Band 4 - Red0.64 - 0.6730
Band 5 - Near Infrared (NIR)0.85 - 0.8830
Band 6 - SWIR 11.57 - 1.6530
Band 7 - SWIR 22.11 - 2.2930
Band 8 - Panchromatic0.50 - 0.6815
Band 9 - Cirrus1.36 - 1.3830
Band 10 - Thermal Infrared (TIRS) 110.60 - 11.19100 (resampled to 30)
Band 11 - Thermal Infrared (TIRS) 211.50 - 12.51100 (resampled to 30)

A vast archive of images is freely available from the U.S. Geological Survey . For more information about how to freely download Landsat images read this .

Color Composite

Often, a combination is created of three individual monochrome images, in which each is assigned a given color; this is defined color composite and is useful for photo interpretation (NASA, 2013). Color composites are usually expressed as:
“R G B = Br Bg Bb”
  • R stands for Red;
  • G stands for Green;
  • B stands for Blue;
  • Br is the band number associated to the Red color;
  • Bg is the band number associated to the Green color;
  • Bb is the band number associated to the Blue color.
The following Figure Color composite of a Landsat 8 image shows a color composite “R G B = 4 3 2” of a Landsat 8 image (for Landsat 7 the same color composite is R G B = 3 2 1) and a color composite “R G B = 5 4 3” (for Landsat 7 the same color composite is R G B = 4 3 2). The composite “R G B = 5 4 3” is useful for the interpretation of the image because vegetation pixels appear red (healthy vegetation reflects a large part of the incident light in the near-infrared wavelength, resulting in higher reflectance values for band 5, thus higher values for the associated color red).
Color composite of a Landsat 8 image
Data available from the U.S. Geological Survey

Congalton, R. and Green, K., 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Boca Raton, FL: CRC Press.
Fisher, P. F. and Unwin, D. J., eds. 2005. Representing GIS. Chichester, England: John Wiley & Sons.
JARS, 1993. Remote Sensing Note. Japan Association on Remote Sensing. Available at
Kruse, F. A., et al., 1993. The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging spectrometer. Data Remote Sensing of Environment.
NASA, 2013. Landsat 7 Science Data User’s Handbook. Available at
Richards, J. A. and Jia, X., 2006. Remote Sensing Digital Image Analysis: An Introduction. Berlin, Germany: Springer.

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