Spectral Bands


Many modern satellites collect infrared and ultraviolet light not visible to the human eye. This information can be used to measure vegetation health, to monitor biomass, or to track forest fires. Newer laser and Radar-based sensors precisely scan the earth, eliminating weather dependencies, producing 3D earth models, and enabling better change detection.

In this imagery over rural Uganda, near-infrared sensors augment imagery to highlight vegetation (False Color) and to measure the health of that vegetation (NDVI). These images show a range of imagery resolutions from different growing seasons.

Each sensor has a specific set of spectral bands designed for the mission of the satellite. While some sensors may have only 3 or 4 bands, others may have dozens, or hundreds (these are called hyperspectral sensors). Many earth-observing satellites include bands that fall within commonly used ‘windows’, such as “red” or “green”.

Common Name Band Range (μm) Landsat 5 Landsat 7 Landsat 8 Sentinel 2 MODIS
Coastal 0.40 - 0.45     1 1  
Blue 0.45 - 0.5 1 1 2 2 3
Green 0.5 - 0.6 2 2 3 3 4
Red 0.6 - 0.7 3 3 4 4 1
Pan 0.5 - 0.7   8 8    
NIR 0.77 - 1.00 4 4 5 8 2
Cirrus 1.35 - 1.40     9 10 26
SWIR16 1.55 - 1.75 5 5 6 11 6
SWIR22 2.1 - 2.3 7 7 7 12 7
LWIR 10.5 - 12.5 6 6 10, 11   31, 32

While the individual band specifications can vary, a “blue” band on any sensor is going to roughly fall within 0.45 and 0.5 microns.

  • Coastal: A coastal band is used for imaging shallow water, detecting fine particles in the atmosphere (aerosols), and measuring subtle changes in ocean color.
  • Red, Green, Blue: Covering the human visible range of 0.4 - 0.7 microns, these bands are the ones most often used for visualization.
  • Pan: A panchromatic is a single band that covers the entire visible range, thereby creating a “black & white” image. Pan bands are useful due to their increased sensitivity (from the larger spectral range), and are also frequently higher resolution than other spectral bands on the same sensor. High resolution pan bands can be used to pan-sharpen on other spectral bands to increase their apparent resolution.
  • NIR: The Near-infrared is beyond the range of human vision but is widely used in a variety of applications it’s ability to separate out water and vegetation.
  • Cirrus: A spectral band in this range has become common on more recent sensors due to it’s ability to detect high altitude clouds (i.e., cirrus clouds) that are invisible in other bands.
  • SWIR16, SWIR22: These two Short-Wave Infrared bands are designated as swir16 and swir22 because there are two windows in the short-wave IR region where the atmosphere is transparent, one is centered around 1.6 microns, and the other centered at 2.2 microns. If a sensor has short-wave IR bands on it, they will be in one or both of these windows. Elsewhere in the short-wave region a satellite can’t see the ground through the atmosphere.
  • LWIR: The long-wave infrared region is used to measure temperature on land or water. Some LWIR sensors cover the entire region from 10.5 to 12.5 microns, while others such as Landsat-8, will split the region up into 2 bands.

These different spectral bands can now be combined in different ways to enhance the contrast between different categories of interest. The most common is to use the Red, Green, and Blue bands to create a natural color image, like what would be seen with the naked eye.

Combination Name Band 1 Band 2 Band 3
Natural Color red green blue
Urban False Color swir22 swir16 red
Agriculture nir red green
Atmospheric Penetration swir22 swir16 nir
Healthy Vegetation nir swir16 blue
Land/Water nir swir16 red
Natural With Atmospheric Removal swir22 nir green
Vegetation Analysis swir16 nir red

Mathematic spectral transformations

In addition to false color composition, spectral bands can be combined mathematically to emphasize a particular set of characteristics. These techniques may draw from all relevant bands, rather than the three band limit set by human vision, to draw out very specific characteristics. This generally demands greater processing of the raw data to minimize noise across the deeper image stack. Mathematical transformations can be used to with good ground data and well-developed algorithms to answer more quantitative questions. While false color can be used to detect the subjective health of agriculture, the proper mathematic transformations might be able to measure the health of each field using a transferrable metric. While false color composites can distinguish mud from water, the proper mathematic transformations could measure how wet the mud is.

Vegetation Indices

Synthesizing data from multiple spectral bands, through ratio or coefficient-based transformations, can produce indices that can be used to compare every point in an image on the same scale. The most common indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), measure vegetation health and distinguish between types of plants, but other indices have been developed to measure burn severity, geologic qualities such as the presence of certain minerals, water turbidity, mud, snow, and more.

Principle Component Analysis

Another image synthesis technique, Principle Component Analysis (PCA), decorrelates the data within each spectral band, such that the most common characteristics of all bands are placed in the highest category and less common characteristics are placed in lower categories, until all variance is explained. It is invaluable for exploration of data and landscape characteristics, simultaneously drawing attention to the most noteworthy and best-hidden features in a scene.

Kauth-Thomas

KT-Transformations are a type of PCA, which, rather than being data-driven, combine information from multiple bands using specially developed coefficients to create the biophysically meaningful variables of Brightness, Greenness, and Wetness- the essential components of a landscape.