Quantifying the impact of spatiotemporal resolution on the interpretation of fluvial geomorphic feature dynamics from Sentinel 2 imagery: an application on a braided river reach in Northern Italy

Machine learning algorithms applied on the publicly available Sentinel 2 images (S2) are opening the opportunity to automatically classify and monitor fluvial geomorphic feature (such as sediment bars or water channels) dynamics across scales. However, there are few analyses on the relative importance of S2 spatial versus temporal resolution in the context of geomorphic research. In a dynamic, braided reach of the Sesia River (Northern Italy), we thus analyzed how the inherent uncertainty associated with S2's spatial resolution (10 m pixel size) can impact the significance of the active channel (a combination of sediment and water) delineation, and how the S2's weekly temporal resolution can influence the interpretation of its evolutionary trajectory. A comparison with manually classified images at higher spatial resolutions (Planet: 3 m and orthophoto: 0.3 m) shows that the automatically classified water is ∼20% underestimated whereas sediments are ∼30% overestimated. These classification errors are smaller than the geomorphic changes detected in the 5 years analyzed, so the derived active channel trajectory can be considered robust. The comparison across resolutions also highlights that the yearly Planet- and S2-derived active channel trajectory are analogous and they are both more effective in capturing the river geomorphic response after major flood events than the trajectory derived from sequential multiannual orthophotos. More analyses of this type, across different types of river could give insights on the transferability of the spatial uncertainty boundaries found as well as on the spatial and temporal resolution trade-off needed for supporting different geomorphic analyses.