Evaluation of Satellite-Derived Bathymetry from High and Medium-Resolution Sensors Using Empirical Methods

Abstract

This study evaluates the accuracy of bathymetric maps generated from multispectral satellite datasets acquired from different multispectral sensors, namely the Worldview 2, PlanetScope, and the Sentinel 2, in the bay of Elounda in Crete. Image pre-processing steps were implemented before the use of the three empirical methods for estimating bathymetry. A dedicated correction and median filter have been applied to minimize noise from the sun glint and the sea waves. Due to the spectral complexity of the selected study area, statistical correlation with different numbers of bands was applied. The analysis indicated that blue and green bands obtained the best results with higher accuracy. Then, three empirical models, namely the Single Band Linear Algorithm, the Multiband Linear Algorithm, and the Ratio Transform Algorithm, were applied to the three multispectral images. Bathymetric and error distribution maps were created and used for the error assessment of results. The accuracy of the bathymetric maps estimated from different empirical models is compared with on-site Single beam Echo Sounder measurements. The most accurate bathymetric maps were obtained using the WorldView 2 and the empirical model of the Ratio Transform algorithm, with the RMSE reaching 1.01 m.

Estimation of shallow bathymetry using Sentinel-2 satellite data and random forest machine learning: a case study for Cheonsuman, Hallim, and Samcheok Coastal Seas

“…In areas with high turbidity, shallow depths are overestimated, and deep depths are underestimated, leading to errors. 33 This was observed in Cheonsu Bay. In regions deeper than 10 m, an inversion phenomenon was observed, where the estimated depth decreased as the actual depth increased.…”

“…The density scatter plot and correlation coefficient also failed to align with the actual depth value distributions. In areas with high turbidity, shallow depths are overestimated, and deep depths are underestimated, leading to errors 33 . This was observed in Cheonsu Bay.…”

“…In addition, the coastal waters around Jeju Island have a mix of sandy and fine-grained shell sediments alongside basalt reefs 31 , 32 . Domestic and international studies have applied various AI techniques to a single study area with clear waters and determined the most appropriate AI approach, 15 , 33 or have applied an AI technique to multiple study areas with marine characteristics less affected by turbidity 5 . However, no study has quantitatively evaluated satellite-based bathymetry results estimated using an SDB model developed with the same methodology for multiple study areas with distinct marine environmental characteristics, such as the West, South, and East Seas of the Korean Peninsula.…”

Deriving Coastal Shallow Bathymetry from Sentinel 2-, Aircraft- and UAV-Derived Orthophotos: A Case Study in Ligurian Marinas

“…The increasing availability of optical images, combined with in situ measurements, allows the application of optical remote-derived bathymetry (RDB), i.e., the derivation of the depths of seas, lakes or rivers, from RS optical images. This can be achieved with state-of-the-art analytical, semi-analytical or empirical methods [22] or, more recently, with machine learning techniques [23]. Examples of newly developed algorithms include S2Shores (Satellite to Shores) [24] which inverts coastal bathymetry from wave kinematics based on the linear dispersion relation, and the combination of a new stereo triangulation method and spectral inversion models [25].…”

Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion

“…The second challenge of deep learning models is determining which spectral bands or band ratios are significant for deep learning models in inverting bathymetry in multispectral or hyperspectral imagery [42]. Typically, bands such as the blue, green, red, or near-infrared bands are used to retrieve bathymetry from multispectral images [43][44][45][46]. Hyperspectral images can contain many combinations of bands and band ratios for the performance of fitting analysis [47][48][49], or they can accurately and quickly measure shallow bathymetry through image data dimensionality reduction [50].…”