**2. Methodology**

In order to test the effectiveness of derived cover fractions at modeling the actual mixed composition of pixels, four post-fire AVIRIS flight lines were processed using MESMA. We used four endmember selection techniques and tested each technique with both the full AVIRIS spectra and a reduced spectral subset determined from a band selection algorithm. The result was eight separate spectral libraries. Each of these eight libraries was used to perform a separate run of the MESMA algorithm (Figure 1). In order to evaluate MESMA derived fractions correspondence with fire severity, two separate validation data sets were used. First, the various cover fractions generated by the eight MESMA runs were compared through linear regression against randomly selected and manually classified plots on WorldView-2 scenes. Using MESMA derived fractions from a spectral library that performed well, the relationship of the cover fraction with two field measurements, Geo Composite Burn Index (GeoCBI), and the percent cover of green, brown, and black trees over an area, was evaluated.

**Figure 1.** Flow chart of methods. Endmembers collected via Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), field spectrometry, and an existing spectral library are used to perform Multiple Endmember Spectral Mixture Analysis (MESMA). Before MESMA, four different means of determining the optimal endmembers from a large spectral library were used: a technique that, for any given endmember within an endmember class, evaluates the count of the other endmembers modeled under an error threshold (In-CoB); a technique that uses the endmember average root mean square error (EAR), minimum average spectral angle (MASA), and In-Cob (EMC); a technique which uses forward-selection to iteratively add or remove spectra until an optimal state is reached (IES); and a technique that post-processes the IES library to produce a more parsimonious result (Reduced IES). Each endmember selection technique is tested with a full AVIRIS spectrum and with a spectral subset. The fractions are tested for goodness of linear fit with the WorldView-2 data via coefficient of determination value (r2) and also compared with field data.
