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首页> 外文期刊>Estuarine Coastal and Shelf Science >Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data
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Satellite-derived bathymetry in optically complex waters using a model inversion approach and Sentinel-2 data

机译：使用模型反转方法和Sentinel-2数据的光学复杂水域中的卫星衍生的沐浴族。

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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.
机译：本研究提出了一种评估光学复杂水域中浅水浴多的模型反演方法，其目的是了解本地化能力，并有助于沿海监测的Sentinel-2的全球评估。分析了沿爱尔兰海岸的三个不同研究区的12个Sentinel-2 MSI图像的数据集。在应用碱基模型之前，测试了两个大气校正程序：深水校正（DWC）和案例2区域沿海颜色（C2RCC）处理器。 DWC在大多数卫星图像中表现出的C2RCC，显示出更一致的结果。使用DWC进行大气校正在应用碱基模型之前，在都柏林湾（RMSE = 1.60，BIAS = -0.51）中发现了最低的平均RMSE，其次是MULROY BAY（RMSE = 1.66，BIAS = 1.30），而布兰登湾则显示最高的平均误差（RMSE = 2.43，BIAS = 1.86）。然而，当考虑最佳图像选择时，偏置小于0.1μm的深度估计，达到+/- 1.40米的差异，可达10米。尽管没有依赖于任何原位深度数据，但这些结果与经验调整方法实现的结果相当。该结论特别相关，因为模型反转方法可能允许未来处理链的关键部分导致改善结果的关键部分。大气校正，选择最佳图像（例如低浊度），适当有限的定义，用于光学成分（Phytoplankton，CDom，Backsfatter）和海底反射的每个像素发生的范围，与对细则特征的理解相结合每个特定的网站都是卫星沐浴族衍生的关键步骤。

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