Spatial patterns ofcommunity composition turnover (beta diversity) may be mapped through generalised dissimilarity modelling (GDM).While remote sensing data are adequate to describe these patterns, the often highdimensional nature of these data poses some analytical challenges, potentially resulting in loss ofgenerality. This may hinder the use ofsuch data for mapping andmonitoring beta-diversity patterns. 2. This study presents Sparse Generalised Dissimilarity Modelling (SGDM), a methodological framework designed to improve the use of high-dimensional data to predict community turnover with GDM. SGDM consists of a two-stage approach, by first transforming the environmental data with a sparse canonical correlation analysis (SCCA), aimed at dealing with high-dimensional data sets, and secondly fitting the transformed data with GDM.The SCCApenalisation parameters are chosen according to a grid search procedure in order to optimise the predictive performance ofaGDMfit on the resulting components. The proposed methodwas illustrated on a case study with a clear environmental gradient of shrub encroachment following cropland abandonment, and subsequent turnover in the bird communities. Bird community data, collected on 115 plots located along the described gradient, were used to fit composition dissimilarity as a function of several remote sensing data sets, including a time series ofLandsat data as well as simulated EnMAP hyperspectral data. 3. The proposed approach always outperformedGDMmodels when fit on high-dimensional data sets. Its usage on low-dimensional data was not consistently advantageous. Models using high-dimensional data, on the other hand, always outperformed those using low-dimensional data, such as single-datemultispectral imagery. 4. This approach improved the direct use of high-dimensional remote sensing data, such as time-series or hyperspectral imagery, for community dissimilarity modelling, resulting in better performing models. The good performance of models using high-dimensional data sets further highlights the relevance of dense time series and data coming from new and forthcoming satellite sensors for ecological applications such as mapping species beta diversity.