Finding mineralogically interesting targets for exploration from spatially coarse visible and near IR spectra

Spectroscopic studies of analog terrestrial mineral assemblages are necessary to develop criteria to identify similar environments on Mars. We use visible/near infrared (VNIR) laboratory, field, and remotely acquired spectral data to identify the iron-bearing and hydrous minerals of Rio Tinto, Spain, an astrobiological analog. Mineralogy evolves from iron sulfate- and oxide-rich (jarosite, rozenite, gypsum, schwertmannite, copiapite, goethite, and hematite assemblages) in young sediments to hydrated iron oxides in preserved terraces.Using spectra from the Rio Tinto, we examine one of the key challenges of extraterrestrial exploration: how to identify promising targets from spatially coarse data for in situ investigation. We apply an index to quantify the expression of spectral diversity as a function of spatial scale from hand sample to landscape. To validate this method for use at the decimeter orbital scale, we apply the index to cm-scale point spectra and meter-scale gridded spectra collected in the field. This exercise in spatial scaling gives increased confidence in the ability of the Spectral Variance Index (SVI) method to locate regions with increased mineral diversity from remotely sensed data. We divide the remotely sensed data into 25 × 25 pixel (200 m × 200 m) cells and calculate the average mean (albedo) and spectral variance over all wavelengths for each cell. We next calculate the expected variance for each cell with a linear regression between mean and spectral variance. The number of standard deviations of each cell's spectral variance is from the expected variance is the SVI value. We locate ∼ 20 areas with high SVI values within the tailing piles and along wide riverbanks downstream of the active mine. This method uses spatially coarse VNIR spectra to recognize areas in Rio Tinto that would be ideal targets for future field exploration, and could also be applied to Mars orbital spectral datasets, such as OMEGA and CRISM.