Mapping topobathymetry at ultra-high spatial resolution using RGB UAV and PlanetScope SuperDove neural network fusion

Abstract. Worldwide coastal areas comprise environmental triple points (air, land and seawater) that cope with coastal risks at unprecedented rates of change. Wind- and wave-related acute hazards add up to the chronic sea-level rise on interface zones that increasingly host human population and assets. Those societal challenges need to be overcome using the most discriminant and finest remote sensors.
We present an innovative two-step methodology to produce an ultra-high spatial resolution (UHSR) topobathymetry using a fusion of a RGB camera mounted on an aerial drone with a multispectral satellite imagery provided with very high temporal resolution. The fusion relied on a DJI Zenmuse P1 (0,08 m pixel size) borne by a DJI Marice 300 RTK, the PlanetScope SuperDove imagery, provided with eight bands at 3 m, and linear or nonlinear (neural network with two hidden layers endowed with three neurons, each) regression. Once the fusion achieved, both topography and bathymetry were mapped using, either the digital surface model (DSM) derived from the drone-derived photogrammetry, or the DSM combined with the UHSR SuperDove imagery. Both datasets served as predictors to model a digital topobathymetric terrain LiDAR response using linear or neural network regression.
The best drone-satellite fusion was completed by the bandwise neural network regression, ranging from R²_test of 0,79 for the purple to 0,94 for the red edge band. The UHSR topobathymetry has been mapped by merging the topography and the bathymetry, distinctly predicted by the combination of the DSM with the UHSR Superdove imagery (R²_test of 0,68 and 0,92, respectively).