The collapse of the tailings pond of the Aznalcállar open pit mine (West of Seville, Spain) in April 1998 left more than 4000 ha of arable land and floodplains contaminated with heavy metal containing pyrite sludge. After a first remediation campaign a considerable contamination remained in the soil. The present study evaluates the possibilities of reflectance spectroscopy and airborne hyperspectral remote sensing for the qualitative and quantitative assessment of heavy metal contamination and the acidification risk related to the mining accident. Based on an extensive data set consisting of geochemical analyses and reflectance measurements of more than 300 soil samples different chemometrics methods (multiple linear regression, partial least squares and artificial neural networks) are tested for computation of concentrations of soil constituents on the basis of the spectral reflectance. Spectral mixture analysis is applied for the analysis of the spatial distribution of the contamination. The abundance information derived from spectral mixture analysis is turned into quantitative information incorporating an artificial mixture experiment. The results of this experiment provide a link between sludge abundance and sludge weight, allowing as a consequence calculation of the amount of residual sludge per pixel, the acidification potential and other parameters important for remediation planning. The application of laboratory, field and imaging spectroscopy for providing quantitative information about the contamination levels in their spatial context is a good complement to conventional methods. The advantage is the reduction of the time and labour-intensive geochemical analysis, because after the model calibration, further samples can be analysed directly with the chemometric models. Furthermore, the spatial distribution can be mapped with imaging spectroscopy data helping in a more precise remediation planning.