Mountainous regions are regarded as significant biodiversity hotspots, offering a diverse range of vital ecosystem services to the communities who reside in them and the surrounding plains. The black grouse (Lyrurus tetrix), a galliform species emblematic of the European Alps, is currently threatened by habitat change. The estimation of population dynamics, and in particular the reproductive success of this species, represents a significant challenge for the long-term conservation of the black grouse. In this study, we attempted to map black grouse brood habitat suitability (BHS) at the scale of an Alpine bioregion, coupling a species distribution model (SDM) with multi-source remote sensing data. To extract landscape composition features likely to influence BHS, convolutional neural networks (CNNs) were employed to characterise very high spatial resolution (VHSR) SPOT6-7 imagery. Altitude, phenological indices derived from Sentinel-2 time series (NDVImax, NDWI1max), and a texture feature derived from the SPOT6-7 images (Haralick entropy) were used to refine landscape characterisation. An SDM based on a random forest ensemble model was used to map black grouse BHS. Altitude, ericaceous heathland and NDVImax emerged as the three most significant variables, consistent with the ecological needs of black grouse. The proportion of ericaceous heathland was especially representative of the foraging needs of female black grouse; the main ecological determinant of habitat suitability for brood rearing with sufficient vegetation cover. This study highlights the effectiveness of integrating VHSR and multispectral time series, together with the advantages offered by deep learning techniques, in extracting species-specific information tailored to conservation issues. The BHS map, produced on a regional scale, constitutes a significant advances in the monitoring of the current population dynamics of black grouse at the western limit of the species’ geographical distribution.