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The impact of changes in vegetation biomass on the global ecosystem and the future evolution of possible climate change is of high relevance. Above-ground biomass (AGB) influences environmental processes, such as the hydrological cycle, soil erosion and degradation, especially in semi-arid areas. Therefore, a great need exists for the development of accurate and transferable methods for biomass estimation in these areas. Remote-sensing-based biomass studies have been carried out since the early 1980s. The large majority of these have focused on forests. A reasonable number of efforts have also been undertaken for the estimation of the biomass in semi-arid regions; however, a summary of these studies is not available yet. This review article provides an overview of the remote-sensing-based research activities for AGB estimation in semi-arid regions using optical data, radar data, combined multi-sensor approaches and modelling approaches. A description of typical field measurement methods is also provided, as well as a summary and discussion of the commonly observed difficulties and challenges to be overcome in the future. Most studies were based on low-and medium-resolution optical or radar data and applied empirical relationships between the remote-sensing-derived indices and biomass field measurements. The influence of soil background on the remote-sensing signals is a major challenge. The biggest challenge, however, seems to be the transferability of the methods in time and space. Especially, empirical relationships seem to provide weak results when applied to another point in time or space. Thus, further research on the transferability of remote-sensing-based methods for biomass estimation - especially in semi-arid areas - is required. Additional analyses and research are also needed for an understanding of the relationship between the AGB and remote-sensing signals in ecosystems with scarce vegetation, towards efficient field sampling schemes, synergetic use of optical and radar data and robust models that are not dependent on extensive field sampling.