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International audience; This is an introduction to the issue: Special feature: improving biodiversity monitoring using satellite remote sensing. The preservation of biodiversity has become a major challenge for sustainable development at national, European (Natura 2000 and Habitats Directive) and international levels (Convention on Biological Diversity, 2011-2020). To address the current conservation needs, there is a need to operationalise methods to assess the distribution of natural resources while integrating information on habitat condition; inform conservation planning and support the assessment of ecosystem services. Increased access to satellite imagery and new developments in data analyses can support progress towards biodiversity conservation targets by stepping up monitoring processes at various spatial and temporal scales. Satellite remote sensing is however no panacea, and little can be achieved without a robust understanding of the socio‐ecological system considered. We aim to demonstrate how the coupling of satellite remote sensing data with ground observations and adequate modelling can provide tangible operational solutions towards a better understanding and management of natural systems.

International audience; 1. Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, especially when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this context, airborne or satellite remote sensing allows information to be gathered over wide areas in a reasonable time. 2. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta‐diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. 3. Extending on previous work, in this manuscript, we propose novel techniques to measure beta‐diversity from airborne or satellite remote sensing, mainly based on: (1) multivariate statistical analysis, (2) the spectral species concept, (3) self‐organizing feature maps, (4) multidimensional distance matrices, and the (5) Rao's Q diversity. Each of these measures addresses one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta‐diversity from remotely sensed imagery and potentially relating them to species diversity in the field.