Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California

We evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody- and non-woody vegetation fractions to assess the comparative benefits of the methods. Results showed that a class-wise empirical approach produced the most spectrally consistent, nearly seamless image mosaics and led to accurate vegetation fraction maps (mean absolute error = 12.6%). Overall, a class-wise empirical approach is recommended as a simple, flexible and transferable technique to compensate for brightness gradients over a global empirical approach, brightness normalization or continuum removal.