Desertification has become a major environmental issue in scientific, political and public circles. Notwithstanding the many inaccurate statements concerning the extension and dynamics of desertification, the fact that dry ecosystems are by nature fragile and susceptible to degradation, and that desertification is to be considered a serious problem, there is now large agreement that the phenomenon is related to particular geographic and physical conditions. The processes are context specific and climate sensitive, and the probability or onset of desertification is a function of biotic and abiotic exchanges at the regional level, and human activity at the local level. While standard methods for identifying and monitoring environmental change in drylands are imperfect or expensive, remote sensing approaches to degradation monitoring can characterise surface properties in terms of physical, bio- and geochemical components with indicator function and link-ages into appropriate process models. Repeated and, by force, standardised observations over longer time periods are indispensable to assess significant changes. The concept of hyperspectral imaging or imaging spectrometry, i.e. the acquisition of surface spectral signatures in a wide wavelength range with numerous narrow and contiguously spaced spectral bands, has meanwhile provided the user community with a range of powerful, yet experimental airborne sensor systems. Considerable efforts have been taken to construct hyperspectral imaging systems which are able to observe the Earth from space orbits. Encouraging results are delivered from the Hyperion sensor on board EOS-1. Nevertheless, none of the existing sensors will allow a long term monitoring of dry ecosystems. In this view, the paper will discuss a concept for developing a hyperspectral satellite mission named 'Spectral Analyses for Dryland Degradation (SAND)' dedicated to the assessment of land degradation in and and semi-arid areas that attempts to combine characteristics of operational earth observation and particular advantages of high spectral resolution systems.