Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data

The techniques used for the spectral unmixing of remotely sensed data are presented. Linear spectral unmixing is a standard technique for spectral mixture analysis that infers a set of pure spectral signatures, called endmembers, and the fractions of these endmembers, called abundances, in each pixel of the scene. The maximum volume procedure adopted by N-FINDR and related algorithms is successful when pure signatures are present in the data, given the available spatial resolution of state-of-the-art imaging spectrometers and the presence of the mixture phenomenon at different scales. Numerous applications related to the monitoring of the environment and the retrieval of biogeophysical parameters have been addressed using spectral unmixing techniques, covering vegetative, soil, urban, and planetary surfaces.