Land cover classification and quantitative analysis of multispectral data in mountainous regions is considerably
hampered by the influence of topography on the spectral response pattern. In the last years, different topographic
correction (TOC) algorithms have been proposed to correct illumination differences between sunny and shaded areas
observed by optical remote sensors. Although the available number of TOC methods is high, the evaluation of their
performance usually relies on the existence of precise land cover information, and a standardised and objective
evaluation procedure has not been proposed yet. Besides, previous TOC assessment studies only considered a limited set of illumination conditions, normally assuming favourable illumination conditions. This paper presents a multitemporal evaluation of TOC methods based on synthetically generated images in order to evaluate the influence of solar angles on the performance of TOC methods. These synthetic images represent the radiance an optical sensor would receive under specific geometric and temporal acquisition conditions and assuming a certain land-cover type. A method for creating synthetic images using state-of-the-art irradiance models has been tested for different periods of the year, which entails a variety of solar angles. Considering the real topography of a specific area a Synthetic Real image (SR) is obtained, and considering the relief of this area as being completely flat a Synthetic Horizontal image (SH) is obtained. The comparison between the corrected image obtained applying a TOC method to a SR image and the SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm. This performance is quantitatively measured through the widely accepted Structural Similarity Index (SSIM) on four selected TOC methods, assessing their behaviour over the year. Among them, C- Correction has ranked first, giving satisfying results in the majority of the cases, while other algorithms show a good performance in summer but give worse results in winter.