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Bathymetry estimated from optical satellite imagery has been increasingly implemented as an alternative to traditional bathymetric survey techniques. The availability of new sensors such as Sentinel-2 with improved spatial and temporal resolution, in comparison with previous optical sensors, offers innovative capabilities for bathymetry derivation. This study presents an assessment of the fit between satellite data and the underlying models in the most widely used empirical algorithms: the linear band model and the log-transformed band ratio model using Sentinel-2A data. Both models were tested in two study areas of the Irish coast with different morphological and environmental conditions. Results showed that the linear band model fitted better than the log-transformed band ratio model providing coefficient of determination values, R2, between 0.83 and 0.88 (0 m-10 m) for the five images considered in the study. The closest fit was found in the depth range 2 m-6 m. Atmospheric correction, bottom type influence, and water column conditions proved to be key factors in the bathymetric derivation using these satellite datasets. [ABSTRACT FROM AUTHOR]

Over the last three decades, satellite imagery has been instrumental in mapping and monitoring water quality. However, satellites often have limitations due to image availability and cloud cover. Today, the spatial resolution of satellite images does not provide finer detail measurements essential for small‐scale water pollution management. Drones offer a complimentary platform capable of operating below cloud cover and acquiring very high spatial resolution datasets in near real‐time. Studies have shown that drone mapping over water can be done via the Direct Georeferencing approach. However, this method is only suitable for high‐end drones with accurate GNSS/IMU. Importantly, this limitation is exacerbated because of the difficulty in placing targets over water, which can be used to improve the accuracy after the survey. This study explored a new method called Assisted Direct Georeferencing which combines the benefits of traditional Bundle Adjustment with Direct Georeferencing. The performance of the approach was evaluated over a variety of different scenarios, demonstrating significant improvement in the planimetric accuracy. From the results, the method reduced the error in XY of drone imagery from MAE of 18.9 to 3.4 m. The result shows the potential of low‐cost drones with Assisted Direct Georeferencing in closing the gap to high‐end drones. [ABSTRACT FROM AUTHOR]

This study presents an assessment of a model inversion approach to derive shallow water bathymetry in optically complex waters, with the aim of both understanding localised capability and contributing to the global evaluation of Sentinel-2 for coastal monitoring. A dataset of 12 Sentinel-2 MSI images, in three different study areas along the Irish coast, has been analysed. Before the application of the bathymetric model two atmospheric correction procedures were tested: Deep Water Correction (DWC) and Case 2 Regional Coastal Color (C2RCC) processor. DWC outperformed C2RCC in the majority of the satellite images showing more consistent results. Using DWC for atmospheric correction before the application of the bathymetric model, the lowest average RMSE was found in Dublin Bay (RMSE = 1.60, bias = −0.51), followed by Mulroy Bay (RMSE = 1.66, bias = 1.30) while Brandon Bay showed the highest average error (RMSE = 2.43, bias = 1.86). However, when the optimal imagery selection was considered, depth estimations with a bias less than 0.1 m and a spread of ±1.40 m were achieved up to 10 m. These results were comparable to those achieved by empirical tuning methods, despite not relying on any in situ depth data. This conclusion is of particular relevance as model inversion approaches might allow future modifications in crucial parts of the processing chain leading to improved results. Atmospheric correction, the selection of optimal images (e.g. low turbidity), the definition of suitably limited ranges for the per-pixel occurrence of optical constituents (phytoplankton, CDOM, backscatter) and seabed reflectances, in combination with the understanding of the specifics characteristics at each particular site, were critical steps in the derivation of satellite bathymetry. • Image selection and atmospheric correction are key for accurate depth estimation. • Depth estimation to 10 m with a bias of <0.1 m and 90% spread of ±1.40 m is achievable. • Site-specific spatial anomalies are ubiquitous and require manual interpretation. • Physics-based methods are a workable solution for survey needs in Irish waters. [ABSTRACT FROM AUTHOR]