Orbital grain size mapping from Sentinel 2 images

Remote Sensing technology is crucial for comprehensive river management capable of monitoring the hydromorphological features of entire river systems. To better understand fluvial processes, a key variable is the bed material grain size distribution and its pattern of change along rivers and over time. Depending on surface characteristics, sunlight reflected from the soil changes as function of many parameters such us surface roughness, linked to geometry and shape of single grains. Building on this evidence, the paper investigates the potential of Sentinel 2 multispectral data for grain size mapping of exposed river sediment bars. The methodology uses near ground UAS imagery in order to correlate local grain sizes to Sentinel 2 radiance values. Results show that it is possible to discriminate broad classes of sediment (from fine gravel to coarse cobbles) from Sentinel 2 data. The most sensitive band capable alone to explain around 70% of the variance is in the SWIR region. The methodology has then been applied on about 500 km of the major Italian river, the Po river. The resulting fining pattern is comparable to others reported in literature and is coherently linked with main tributaries and river infrastructures existing along its course. This method represents potentially a major advance in our current ability to characterize fluvial habitats and processes along major river systems, thanks to the characteristics of Sentinel 2 data, free available worldwide with a time frequency of about 5 days. The proposed methodology opens to the possibility to routinely map grain size classes along any large river areas thus generating broad perspectives for future fluvial survey practices