Global Satellite-Based Coastal Bathymetry from Waves

Almar, Rafaël; Bergsma, Erwin W. J.; Thoumyre, Grégoire; Baba, Mohamed Wassim; Cesbron, Guillaume; Daly, Christopher; Garlan, Thierry; Lifermann, Anne

doi:10.3390/rs13224628

Cited by 25 publications

(14 citation statements)

References 67 publications

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“…As an alternative, bathymetry can be derived from satellite wave kinematics (Fig. 7), which has the potential to be independent of the water column and bottom spectral characteristics (Bergsma et al 2019;Daly et al 2021;Baba et al 2021;Almar et al 2021b) to depths of 35-50 m, depending on wave conditions Almar 2020, Daly et al 2021). The nearshore of coastal areas includes within the same cell and morphological profile both emergent and submerged portions that rapidly exchange sediment.…”

Section: Bathymetry Toward a Shoreface Land-sea Coastal Morphology Co...mentioning

confidence: 99%

Coastal Zone Changes in West Africa: Challenges and Opportunities for Satellite Earth Observations

et al. 2022

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The West African coastal population and ecosystems are increasingly exposed to a multitude of hazards. These are likely to be exacerbated by global climate change and direct impacts from local human activities. Our ability to understand coastal changes has been limited by an incomplete understanding of the processes and the difficulty of obtaining detailed data. Recent advances in satellite techniques have made it possible to obtain rich coastal data sets that provide a solid foundation for improving climate change adaptation strategies for humanity and increasing the resilience of ecosystems for sustainable development. In this article, we review West African coastal layout and current socio-environmental challenges together with key parameters that can be monitored and several coastal management programs that rely on satellite techniques to monitor indicators at the regional level. The social, technical and scientific problems and difficulties that hinder the interest of coastal practitioners and decision-makers to use the satellite data are identified. We provide a roadmap to precisely respond to these difficulties and on how an improved satellite earth observation strategy can better support future coastal zone management in West Africa.

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Section: Bathymetry Toward a Shoreface Land-sea Coastal Morphology Co...mentioning

confidence: 99%

Coastal Zone Changes in West Africa: Challenges and Opportunities for Satellite Earth Observations

et al. 2022

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“…Methods based on the radiative transfer of light in water as a function of depth and wavelength (i.e., color-based methods) can be used to estimate depth in optically shallow waters [17][18][19][20][21][22]. Such methods are sensitive to the optical properties of seawater and are generally limited to clear and non-turbid waters [1,23]. Other methods based on wave kinematics extract wave features from satellite imagery such as the wave phase shift and wave number to estimate depth using the linear dispersion relation [24] (described in more detail in Section 2).…”

Section: Introductionmentioning

confidence: 99%

“…Methods based on the radiative transfer of light in water are more accurate in shallow waters (up to 15 m depth) and are able to detect smaller-scale bathymetric features, with an absolute error order of 10-20% of the target value, and an average RMSE of 1.5 m [25][26][27]. On the other hand, wave kinematics-based approaches are preconditioned on the observability of wave patterns in the input imagery, however, their detectable depth range is significantly larger than the typical range of color-based methods [20] but with less accuracy when applied globally (RMSE between 6-9 m, [23]). The task of constructing a depth estimation function applicable to satellite data is non-trivial and remains a topic of ongoing research due to the great potential it offers to in-expensively monitor coastal morphodynamics at a large scale.…”

Section: Introductionmentioning

confidence: 99%

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

et al. 2022

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The ability to monitor the evolution of the coastal zone over time is an important factor in coastal knowledge, development, planning, risk mitigation, and overall coastal zone management. While traditional bathymetry surveys using echo-sounding techniques are expensive and time consuming, remote sensing tools have recently emerged as reliable and inexpensive data sources that can be used to estimate bathymetry using depth inversion models. Deep learning is a growing field of artificial intelligence that allows for the automatic construction of models from data and has been successfully used for various Earth observation and model inversion applications. In this work, we make use of publicly available Sentinel-2 satellite imagery and multiple bathymetry surveys to train a deep learning-based bathymetry estimation model. We explore for the first time two complementary approaches, based on color information but also wave kinematics, as inputs to the deep learning model. This offers the possibility to derive bathymetry not only in clear waters as previously done with deep learning models but also at common turbid coastal zones. We show competitive results with a state-of-the-art physical inversion method for satellite-derived bathymetry, Satellite to Shores (S2Shores), demonstrating a promising direction for worldwide applicability of deep learning models to inverse bathymetry from satellite imagery and a novel use of deep learning models in Earth observation.

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“…Conventional field measurements are labour-intensive, causing bathymetry data to often be outdated or even absent for large parts of the global coastline. Space-based monitoring offers the opportunity to fill this data gap on a global scale, with satellites having global coverage and potentially allowing bathymetry measurements on large spatial scales [6] at daily to weekly return intervals [7,8]. Bathymetry can be derived from optical satellite imagery of the water surface by estimating depths from color differences [9,10] or from wave characteristics [11].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques have reported accuracies between <1 m to 2.6 m, yet these numbers are based on specific analyses of one or two field cases. When such techniques are generally applied to more field sites, the accuracy can drop to errors of 6-9 m [6].…”

Section: Introductionmentioning

confidence: 99%

Depth Inversion from Wave Frequencies in Temporally Augmented Satellite Video

et al. 2022

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Optical satellite images of the nearshore water surface offer the possibility to invert water depths and thereby constitute the underlying bathymetry. Depth inversion techniques based on surface wave patterns can handle clear and turbid waters in a variety of global coastal environments. Common depth inversion algorithms require video from shore-based camera stations, UAVs or Xband-radars with a typical duration of minutes and at framerates of 1–2 fps to find relevant wave frequencies. These requirements are often not met by satellite imagery. In this paper, satellite imagery is augmented from a sequence of 12 images of Capbreton, France, collected over a period of ∼1.5 min at a framerate of 1/8 fps by the Pleiades satellite, to a pseudo-video with a framerate of 1 fps. For this purpose, a recently developed method is used, which considers spatial pathways of propagating waves for temporal video reconstruction. The augmented video is subsequently processed with a frequency-based depth inversion algorithm that works largely unsupervised and is openly available. The resulting depth estimates approximate ground truth with an overall depth bias of −0.9 m and an interquartile range of depth errors of 5.1 m. The acquired accuracy is sufficiently high to correctly predict wave heights over the shoreface with a numerical wave model and to find hotspots where wave refraction leads to focusing of wave energy that has potential implications for coastal hazard assessments. A more detailed depth inversion analysis of the nearshore region furthermore demonstrates the possibility to detect sandbars. The combination of image augmentation with a frequency-based depth inversion method shows potential for broad application to temporally sparse satellite imagery and thereby aids in the effort towards globally available coastal bathymetry data.

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Global Satellite-Based Coastal Bathymetry from Waves

Cited by 25 publications

References 67 publications

Coastal Zone Changes in West Africa: Challenges and Opportunities for Satellite Earth Observations

Coastal Zone Changes in West Africa: Challenges and Opportunities for Satellite Earth Observations

Coastal Bathymetry Estimation from Sentinel-2 Satellite Imagery: Comparing Deep Learning and Physics-Based Approaches

Depth Inversion from Wave Frequencies in Temporally Augmented Satellite Video

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