EMPIRICAL SATELLITE-DERIVED BATHYMETRY (SDB) FOR A SECTION OF THE EASTERN CONTINENTAL SHELF OF RIO GRANDE DO NORTE: A CASE STUDY IN A SHALLOW TROPICAL ENVIRONMENT

Empirical satellite-derived bathymetry (SDB) for a section of the eastern continental shelf of Rio Grande do Norte: a case study in a shallow tropical envionment

Abstract

Shallow continental shelves are coastal zones influenced by currents, waves, and tides, directly associated with erosion and sedimentation processes, although characterized by a significant lack of available data. The shelf relief is generally obtained through hydroacoustic geophysical methods, which, although accurate, involve high costs, time consumption, and logistical limitations, especially in areas with adverse oceanographic conditions that hinder vessel-based surveys. In this context, empirical Satellite-Derived Bathymetry (SDB) emerges as a cost-effective and complementary alternative to traditional methods, allowing preliminary mapping with high spatial and temporal resolution. This study applied empirical SDB to map an area of the eastern continental shelf of the State of Rio Grande do Norte, a region marked by outcrops and reef banks that hinder navigation and in situ data collection. The methodology was based on the use of underwater reflectance from visible spectral bands to estimate depths, calibrated using nautical chart data provided by the Brazilian Navy. Satellite images were processed in a GIS environment, employing the model proposed by Stumpf et al. (2003) using data from Sentinel-2 MSI and Landsat-8 OLI sensors. The best performance was observed with Sentinel images, yielding a coefficient of determination (R²) of 0.90, while Landsat images presented an R² of 0.83. The technique proved applicable in areas with depths of up to 30 meters. Initial noise was mitigated through the application of median filters, improving the quality of the generated profiles. The results highlight both the potential and the limitations of SDB as an auxiliary tool for expanding bathymetric knowledge in constrained coastal environments.