ABSTRACT Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches by extending them via spatial information. In this study, the relationship between multibeam depth and Sentinel-2 image bands was analyzed in an optically complex environment using the spatial predictor of kriging with an external drift (KED), where its external drift component was estimated: a) by a ratio of log-transformed bands based on Stumpf’s model (KED_S) and b) by a log-linear transform based on Lyzenga’s model (KED_L). Through the calibration of KED models, the study objectives were: 1) to better understand the empirical relationship between Sentinel-2 multispectral satellite reflectance and depth, 2) to test the robustness of KED to derive bathymetry in a multitemporal series of Sentinel-2 images and multibeam data, and 3) to compare the performance of KED against the existing non-spatial models described by Stumpf et al. and Lyzenga. Results showed that KED could improve prediction accuracy with a decrease in RMSE of 89% and 88%, and an increase in R2 of 27% and 14%, over the Stumpf and Lyzenga models, respectively. The decrease in RMSE provides a worthwhile improvement in accuracy, where results showed effective prediction of depth up to 6 m. However, the presence of higher concentrations of suspended materials, especially river plumes, can reduce this threshold to 4 m. As would be expected, prediction accuracy could be improved through the removal of outliers, which were mainly located in the channel of the river, areas influenced by the river plume, abrupt topography, but also very shallow areas close to the shoreline. These areas have been identified as conflictive zones where satellite-derived bathymetry can be compromised.

中文翻译：

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使用 Sentinel 2 图像和空间预测模型了解卫星测深

摘要 光学卫星数据是一种有效的补充水文调查的方法，可用于推导浅海水域的测深。实证方法（特别是 Stumpf 和 Lyzenga 的模型）提供了一种从遥感中获取测深信息的实用方法。然而，最近的研究集中在通过空间信息扩展这些经验方法来提高它们的性能。在本研究中，使用具有外部漂移 (KED) 的克里金空间预测器在光学复杂环境中分析了多波束深度与 Sentinel-2 图像波段之间的关系，其中估计其外部漂移分量：基于 Stumpf 模型 (KED_S) 和 b) 通过基于 Lyzenga 模型 (KED_L) 的对数线性变换的对数转换带。通过对 KED 模型的校准，研究目标是：1) 更好地理解 Sentinel-2 多光谱卫星反射率和深度之间的经验关系，2) 测试 KED 在 Sentinel-2 多时相系列图像中推导水深测量的稳健性和多波束数据，以及 3) 将 KED 的性能与 Stumpf 等人描述的现有非空间模型进行比较。和利曾加。结果表明，与 Stumpf 和 Lyzenga 模型相比，KED 可以提高预测精度，RMSE 分别降低 89% 和 88%，R2 分别增加 27% 和 14%。RMSE 的降低为准确度提供了有价值的改进，结果显示对深度高达 6 m 的有效预测。然而，悬浮物质的浓度较高，尤其是河流羽流，可以将此阈值降低到 4 m。正如预期的那样，可以通过去除异常值来提高预测精度，这些异常值主要位于河道、受河流羽流影响的区域、陡峭的地形以及靠近海岸线的非常浅的区域。这些地区已被确定为冲突地区，在这些地区，卫星得出的水深测量可能会受到影响。