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.

There are several well-known combinations that are optimized to provide maximum contrast between categories of interest under various use cases.

Combination Name Red Wavelength Green Wavelength Blue Wavelength LS 8 Bands
Natural Color (actual RGB) 0.64-0.67µm 0.53-0.59µm 0.45-0.51µm 4 3 2
False Color (urban) 2.11-2.29µm 1.57-1.65µm 0.64-0.67µm 7 6 4
Agriculture 0.85-0.88µm 0.64-0.67µm 0.53-0.59µm 5 4 3
Atmospheric Penetration 2.11-2.29µm 1.57-1.65µm 0.85-0.88µm 7 6 5
Healthy Vegetation 0.85-0.88µm 1.57-1.65µm 0.45-0.51µm 5 6 2
Land/Water 0.85-0.88µm 1.57-1.65µm 0.64-0.67µm 5 6 4
Natural With Atmospheric Removal 2.11-2.29µm 0.85-0.88µm 0.53–0.59µm 7 5 3
Vegetation Analysis 1.57-1.65µm 0.85-0.88µm 0.64-0.67µm 6 5 4

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.