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.

这项研究提出了一种模型反演方法的评估，该模型反演方法用于导出光学复杂水中的浅水测深法，旨在了解局部能力并有助于对Sentinel-2进行海岸监测的全球评估。已分析了爱尔兰沿岸三个不同研究区域的12张Sentinel-2 MSI图像的数据集。在应用测深模型之前，先测试了两种大气校正程序：深水校正（DWC）和案例2区域沿海颜色（C2RCC）处理器。DWC在大多数显示更一致结果的卫星图像中均优于C2RCC。在应用测深模型之前使用DWC进行大气校正，发现都柏林湾的最低平均均方误差（RMSE = 1.60，偏差= -0.51），其次是Mulroy Bay（RMSE = 1.66，偏差= 1.30），而布兰登湾则显示出最高的平均误差（RMSE = 2.43，偏差= 1.86）。但是，当考虑最佳图像选择时，直到10 m时，深度估计的偏差都小于0.1 m，扩展范围为±1.40 m。尽管不依赖任何现场深度数据，但这些结果与通过经验调整方法获得的结果相当。该结论具有特殊意义，因为模型反演方法可能允许将来在加工链的关键部分进行修改，从而导致结果改善。大气校正，最佳图像的选择（例如低浊度），光学成分（浮游植物，CDOM，反向散射）的每像素出现的适当限制范围的定义以及海床反射率，