Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data
Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data
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DOI：
10.1016/j.ecss.2020.106814
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发表时间：
2020-08
影响因子：
2.8
通讯作者：
G. Casal;J. Hedley;X. Monteys;P. Harris;C. Cahalane;T. McCarthy
中科院分区：
文献类型：
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作者：
G. Casal;J. Hedley;X. Monteys;P. Harris;C. Cahalane;T. McCarthy
本研究提出了一个模型反演方法，以获得浅水水深在光学复杂的沃茨，既了解本地化的能力，并有助于全球评价哨兵-2沿海监测的目的进行评估。一个数据集的12个哨兵-2 MSI图像，在三个不同的研究领域沿着爱尔兰海岸，进行了分析。在应用测深模型之前，对两种大气校正程序进行了测试：深水校正（DWC）和案例2区域海岸颜色（C2 RCC）处理器。DWC在大多数卫星图像中的表现优于C2 RCC，显示出更一致的结果。在应用测深模型之前使用DWC进行大气校正，发现都柏林湾的平均RMSE最低（RMSE = 1.60，偏差=-0.51），其次是Mulroy湾（RMSE = 1.66，偏差= 1.30），而布兰登湾的平均误差最高（RMSE = 2.43，偏差= 1.86）。然而，当考虑到最佳图像选择时，在10 m范围内实现了偏差小于0.1 m和展布为±1.40 m的深度估计。这些结果与经验调整方法所取得的结果相当，尽管不依赖于任何原位深度数据。这一结论是特别相关的模型反演方法可能允许未来的修改，在处理链的关键部分，从而改善结果。大气校正、选择最佳图像（例如低浊度图像）、确定光学成分（浮游植物、CDOM、反向散射）和海底反射率每像素出现的适当有限范围，再加上了解每个特定地点的具体特征，这些都是得出卫星测深数据的关键步骤。
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.