The successful management of fine-grained coastal environments relies on an understanding of, and an ability to predict, their likely future behavior. The latter depends not only on the hydrodynamic conditions to which these coastal systems are exposed, but also on characteristic surface properties that determine erosion shear strength and thus surface stability. Due to the inaccessibility of intertidal areas, precise ground-based measurements of mudflat stability are difficult to conduct. Remote sensing can provide full spatial coverage and non-intrusive measurement. As stability changes on mudflats are linked to subtle differences in mudflat surface characteristics, they can potentially be mapped by hyperspectral data. This paper reports on a study aimed at assessing the suitability of hyperspectral data for estimating mudflat characteristics related to stability. Hyperspectral images were collected along with near contemporary ground measurements. An unsupervised classification resulted in a map that reproduced observed thematic and topographic features, thus highlighting the close link between topography and surface sedimentary characteristics resulting in distinct spectral signatures. Multiple regression analysis was used to relate surface characteristics to hyperspectral data to construct regression equations. Erosion shear stress was estimated directly from the hyper-spectral data and also by a relationship with surface characteristics. The results of the thematic class map matched well with the known situation at the site during image acquisition. The quantitative information on surface characteristics suggest that the use of hyperspectral data for the assessment of erosion shear stress, surface stability, and thus the likely future behaviour in this dynamic environment can contribute to a significant improvement in the information needed for the successful management of shallow coastal systems.