Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data

Casal, Gema; Monteys, Xavier; Hedley, John D.; Harris, Paul; Cahalane, Conor; McCarthy, Tim

doi:10.1080/01431161.2018.1533660

Cited by 76 publications

(62 citation statements)

References 43 publications

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“…In addition, because of the different reflectance of the sediments, the situation of positive or negative water depth value situation will also occur. This result is similar to the conclusions of previous researchers [37,38]. In the very shallow offshore waters, a negative water depth may be estimated.…”

Section: Discussionsupporting

confidence: 92%

Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Zhang

2020

Remote Sensing

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Bathymetric surveys are of great importance for submarine topography mapping and coastal construction projects. They are also of great significance for terrain surveys of islands and coastal zones, maritime navigation and marine management planning. Traditional ship-borne water depth measurement methods are costly and time-consuming, therefore, in recent years, passive optical remote sensing technology has become an important means for shallow water depth measurements. In addition, multispectral water depth optical remote sensing has wide application values. Considering the relationship between water depth and the inherent optical characteristics of water column, an inherent optical parameters linear model (IOPLM) is developed to estimate shallow water bathymetry from high spatial resolution multispectral images. Experiments were carried out in the shallow waters (≤20 m) around Dongdao Island in China’s Paracel Islands and Saipan Island in the Northern Mariana Islands. Different accuracy evaluation indexes were used to verify the model. The comparisons with the traditional log-linear model and the Stumpf model show that in terms of overall accuracy and accuracy in different water depths, the IOPLM has slightly better results and stronger retrieval capabilities than the other models. The mean absolute error (MAE) of Dongdao Island and Saipan Island reached 1.17 m and 1.92 m, and the root mean square error (RMSE) was 1.49 m and 2.4 m, respectively.

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Section: Discussionsupporting

confidence: 92%

Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Zhang

2020

Remote Sensing

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“…The multi-scene strategy applied here did not require any screening or manual adjustment of the imagery prior to compositing. It automatically picked the pixels least impacted by turbidity (e.g., Figure 8) from the set of scenes provided (Table 1), substantially simplifying the effort compared with other studies where the selection of optimal images with variation in the water quality conditions were essential in the extraction of SDB [20,22,29,55]. Manual selection of the optimal scene is not only highly subjective, but requires considerable time and effort, and may still include regions having patches of turbidity.…”

Section: Discussionmentioning

confidence: 99%

Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission

Caballero

Stumpf

2020

Remote Sensing

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Satellite-Derived Bathymetry (SDB) has significant potential to enhance our knowledge of Earth's coastal regions. However, SDB still has limitations when applied to the turbid, but optically shallow, nearshore regions that encompass large areas of the world's coastal zone. Turbid water produces false shoaling in the imagery, constraining SDB for its routine application. This paper provides a framework that enables us to derive valid SDB over moderately turbid environments by using the high revisit time (5-day) of the Sentinel-2A/B twin mission from the Copernicus programme. The proposed methodology incorporates a robust atmospheric correction, a multi-scene compositing method to reduce the impact of turbidity, and a switching model to improve mapping in shallow water. Two study sites in United States are explored due to their varying water transparency conditions. Our results show that the approach yields accurate SDB, with median errors of under 0.5 m for depths 0-13 m when validated with lidar surveys, errors that favorably compare to uses of SDB in clear water. The approach allows for the semi-automated creation of bathymetric maps at 10 m spatial resolution, with manual intervention potentially limited only to the calibration to the absolute SDB. It also returns turbidity data to indicate areas that may still have residual shoaling bias. Because minimal in-situ information is required, this computationally-efficient technique has the potential for automated implementation, allowing rapid and repeated application in more environments than most existing methods, thereby helping with a range of issues in coastal research, management, and navigation. surveys are constrained by access, logistics and extremely high deployment cost. It is estimated that multibeam echo sounding (at best resolution) would take more than 200 ship-years and billions of dollars to complete a swath survey of the seafloor [6]. The problem is substantial; consider that some 50% of the USA territories-as an example-were surveyed by using old hydrographic methods that do not meet today's requirement [7]. In the opinion of the IHO, sea bottom information derived from satellite imagery, widely known as Satellite-Derived-Bathymetry (SDB), should be considered as a potential technology to improve the collection, timeliness, quality, and availability of bathymetric data worldwide. In this regard, IHO has started to evaluate SDB strategies and the IHO S-44 standards are currently under revision. Furthermore, SDB offers a low-cost and non-intrusive suitable solution because no mobilization is required, removing health and safety risk, and any environmental impact.Approaches to SDB mapping vary on aim and rationale, spatial scale, and source of satellite system information. The concept is based on detection of sunlight reflected from the seafloor, and algorithms that use spectral information from this light to calculate water column depth. Several reviews of the methodologies are available in the literature [8,9]. Although the acquisition of imagery ...

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“…The frontier of SDB research has advanced from basic linear functions into band ratios of log transformed models (Lyzenga, 1981), non-linear inverse models (Stumpf et al, 2003) and physics-based methods similar to radiative transfer models (Dekker et al, 2011). Empirical SDB prediction methods have been assessed for deriving bathymetry in Irish waters in previous tests (Coveney and Monteys, 2011; Monteys et al, 2015; Casal et al, 2019) and although SDB performance varies depending on the approach, the prediction differences were approximately 10% of water depth, and were influenced by water type and by sensor types.…”

Section: Introductionmentioning

confidence: 99%

A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry

Cahalane

Magee

Monteys

et al. 2019

Remote Sensing of Environment

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Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement of optical penetration of the water column. The resolution of bathymetric mapping and achievable horizontal and vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, water quality and other environmental conditions. Efforts to improve accuracy include physics-based methods (similar to radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling of the seabed and water column, but the spatial component of SDB measurements is often under-utilised in SDB workflows despite promising results suggesting potential to improve accuracy significantly. In this study, a selection of satellite datasets (Landsat 8, RapidEye and Pleiades) at different spatial and spectral resolutions were tested using a log ratio transform to derive bathymetry in an Atlantic coastal embayment. A series of non-spatial and spatial linear analyses were then conducted and their influence on SDB prediction accuracy was assessed in addition to the significance of each model's parameters. Landsat 8 (30 m pixel size) performed relatively weak with the non-spatial model, but showed the best results with the spatial model. However, the highest spatial resolution imagery used – Pleiades (2 m pixel size) showed good results across both non-spatial and spatial models which suggests a suitability for SDB prediction at a higher spatial resolution than the others. In all cases, the spatial models were able to constrain the prediction differences at increased water depths.

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Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data

Cited by 76 publications

References 43 publications

Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Shallow Water Bathymetry Based on Inherent Optical Properties Using High Spatial Resolution Multispectral Imagery

Towards Routine Mapping of Shallow Bathymetry in Environments with Variable Turbidity: Contribution of Sentinel-2A/B Satellites Mission

A comparison of Landsat 8, RapidEye and Pleiades products for improving empirical predictions of satellite-derived bathymetry

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