Abstract

Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying.

摘要

近岸水深测量的准确图表对于贸易、渔业、旅游和海洋能源行业经常光顾的沿海水道的安全和可靠使用至关重要。卫星图像的可访问性和各种卫星衍生测深 (SDB) 技术的可用性为传统的原位测深测量提供了一种具有成本效益的替代方案。此外，改进的算法和机器学习模型的进步为更高质量的测深推导提供了机会。然而，迄今为止，传统的基于物理的技术、改进的基于物理的方法和机器学习集成模型之间的相对准确性和性能尚未得到充分量化。在这项研究中，近岸水深测量来自 La Parguera 附近的 Sentinel-2 卫星图像，波多黎各利用传统的带比算法、带比切换方法、随机森林机器学习模型和 XGBoost 机器学习模型。机器学习模型返回了可比较的结果，并且相对于其他技术明显更准确；然而，这两种机器学习模型都需要大量的训练数据集。所有模型都受到环境影响和图像空间分辨率的限制，这些因素被评估为常规使用卫星测深作为一种可靠的水文测量方法的限制因素。机器学习模型返回了可比较的结果，并且相对于其他技术明显更准确；然而，这两种机器学习模型都需要大量的训练数据集。所有模型都受到环境影响和图像空间分辨率的限制，这些因素被评估为常规使用卫星测深作为一种可靠的水文测量方法的限制因素。机器学习模型返回了可比较的结果，并且相对于其他技术明显更准确；然而，这两种机器学习模型都需要大量的训练数据集。所有模型都受到环境影响和图像空间分辨率的限制，这些因素被评估为常规使用卫星测深作为一种可靠的水文测量方法的限制因素。