The improved spatial and temporal resolution of latest-generation Earth Observation missions, such as Landsat 8 and Sentinel-2, has increased the potential of remote sensing for mapping land surface phenology in inland water systems. The ability of a time series of medium-resolution satellite data to generate quantitative information on macrophyte phenology was examined, focusing on three temperate shallow lakes with connected wetlands in Italy, France, and Romania. Leaf area index (LAI) maps for floating and emergent macrophyte growth forms were derived from a semi-empirical regression model based on the best-performing spectral index, with an error level of 0.11 m 2 m -2 . Phenology metrics were computed from LAI time series using TIMESAT to analyze the seasonal dynamics of macrophyte spatial distribution patterns and species-dependent variability. Particular seasonal patterns seen in the autochthonous and allochthonous species across the three study areas related to local ecological and hydrological conditions. How characteristics of the satellite dataset (cloud cover threshold, temporal resolution, and missing acquisitions) influenced the phenology metrics obtained was also assessed. Our results indicate that, with a full-resolution time series (5-day revisit time), cloud cover introduced a bias in the phenology metrics of less than 2 days. Even when the temporal resolution was reduced to 15 days (like the Landsat revisit time) the timing of the start and the peak of macrophyte growth could still be mapped with an error of no more than 2-3 days.

Thanks to the improved spatial and temporal resolution of new generation Earth Observation missions, such as Landsat 8 and Sentinel-2, the capabilities demonstrated in the last decades by remote sensing in mapping land surface phenology of terrestrial biomes can now be tested in inland water systems.We assessed the capabilities of dense time series of medium resolution satellite data to deliver information about quantitative macrophyte phenology metrics, focusing on three temperate European shallow lakes with connected wetlands: Mantua lakes system (Italy) Lac de Grand-Lieu (France), and Fundu Mare Island (Romania).Macrophyte leaf area index (LAI) maps were derived from semi-empirical regression modelling based on the best performing spectral index, with an error level around 0.1 m 2 m -2 . Phenology metrics computed from LAI time series using TIMESAT code were used to analyse macrophyte seasonal dynamics across the three study areas in terms of spatial patterns and species-dependent variability for the year 2015. These peculiar dynamicity patterns of autochthonous and allochthonous species were related to the environmental characteristics of each area in terms of ecological, hydrological and meteorological conditions. In addition, the influence of cloud cover thresholding, temporal resolution and missing acquisitions was assessed in terms of phenology timing metrics retrieval, thus providing quantitative information on the expected variability of TIMESAT outputs when time series with reduced resolution are used, i.e. if 16-day time revisit Landsat data were used for retrospective study of macrophyte phenology during the last three decades.

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