Abstract: Technological advances in bathymetric equipment, positioning capacity, data processing, as well as the development of new ways of obtaining depth and other ways of exploring the submerged bottom, have been noticed in recent years. It is known that acoustic remote sensing is the most widely used technique for depth measurement. Survey systems can be embedded on various platforms and also provide different accuracies. Coupled to these systems are also Global Navigation Satellite System (GNSS), auxiliary sensors and speed profilers, improving the accuracy of the data obtained. Alternatively to the use of echo sounders, optical sensing (active and passive sensors) or satellite radar altimetry can be used to estimate depth. Thus, this study aims to present an overview of bathymetric survey methodologies, as well as the evolution of the use of sounding platforms, systems and sensors and various existing technologies. In addition, the main uncertainties involved and the advantages and disadvantages of the available solutions are also evidenced, providing the reader the ability to choose the most appropriate technique.

Abstract: The use of acoustic systems for mapping submerged areas is the most accurate way. However, echosounders are expensive and, in addition, the equipment requires a great deal of experience on the part of the specialist. From another perspective, orbital and aerial images (acquired by RPA’s- Remotely Piloted Aircraft) can offer bathymetric maps of larger locations that are difficult to access at a low operating cost. Therefore, the present study’s main objective was to evaluate the utility of RGB images obtained with RPA’s in water reservoirs. Thus, Artificial Neural Networks were used for depth training and prediction. Subsequently, it compared to the bathymetric data from the same pond in question, raised from acoustic sensors, quantifying the vertical uncertainty through three estimators. Regarding the statistical analysis, the RMSE and Ф estimators showed better reliability. The 300-point sample showed the best quality in processing. The results showed that the methodology could improve the management of water resources. The method allows reduced execution time and lowers cost, especially for using only the green, red and blue channels, easily found in most cameras coupled to RPA’s.