Spectral Library: Overview

Spectral mixture analysis (SMA) is an image processing technique for estimating cover fractions of surface component materials at the subpixel level from hyperspectral or multispectral imagery. It assumes that pixel reflectance is a function of the proportion of surface materials that comprise that pixel. A commonly applied algorithmic constraint of SMA is that these proportions sum to unity. Cover fractions of green vegetation, non-photosynthetic vegetation, soil, etc. vary greatly at fine (subpixel) scales, in both burned and unburned scenes. Since SMA is based on this valid assumption, SMA can be usefully applied whether the resolution is 500 m (e.g., MODIS satellite) or 5 m (e.g., airborne hyperspectral).

Since SMA estimates cover fractions directly, the outputs are more intuitive to those not familiar with remote sensing terms and techniques. Thus, a soil fractional cover map derived from SMA can be less subject to misinterpretation than a burn severity index such as the Normalized Burn Ratio (NBR), from which one must then infer the biophysical characteristic of interest (e.g., percent soil exposure). The major disadvantage of SMA-based cover fraction maps relative to NBR-based maps is that the latter can be produced quickly, with minimal image processing, while the application of SMA requires collection or derivation of spectral endmembers.

Dead Aspen Spectral graphic