Gravel bars are dynamic areas of bedload deposition in rivers. They perform important ecological functions and are considered indicators of changes in the hydrological characteristics of rivers. Satellite images with a frequent revisit period and a large area of simultaneous coverage are an ideal data source for monitoring many natural features including gravel bars. Openly and freely available remote sensing data from the Sentinel-2 and Landsat systems have a spatial resolution that may be too coarse for accurate detection of gravel bars, especially in mountainous areas where rivers and related features are narrow. We therefore developed a sub-pixel mapping method based on spectral mixture analysis. Very high resolution aerial orthophotos and satellite images, as well as field mapping, were used as reference. Sentinel-2 and Landsat spectral bands were supplemented with spectral indices to increase the separability between land cover classes of interest. Automatically selected endmembers led to results with similar accuracy as when manually selected endmembers were used. Endmembers selected on one image of the study area during the leaf-on season could be used to analyse another image of the same study area acquired with the same remote sensing system at a different time. The fraction maps were found to be more accurate than maps produced by hard classification with Spectral Angle Mapper using the same input data. Considering these findings, we produced fraction maps of gravel, vegetation, and water presence for the Soča and Sava rivers in Slovenia, and the Vjosa river in Albania for a period of over 30 years. The thematic accuracy of the maps was within 90%. We also tested the ability of fraction maps for change detection and found that changes of at least 400 m2 could be accurately detected. The time series plots can also be used to detect gravel removal as demonstrated at known excavation sites near the Dolje settlement on Soča and near Kranj on Sava. The dissertation contributes to science with new insights about the application of sub-pixel mapping for monitoring natural processes. The developed method can be applied to study areas where less in situ data are available. More informed management decisions can be made based on newly acquired knowledge.