Bathymetry is crucial for coastal research, infrastructure development, resource exploitation, risk management, defense applications and climate impact analysis. However, traditional in-situ surveys are often prohibitively expensive, time-consuming, and impractical in inaccessible waters. As a result, despite its importance, comprehensive bathymetric data remains scarce beyond localised studies. The growing availability of satellite missions, coupled with improvements in resolution, revisit frequency, and spectral bands, enables satellite data as a powerful alternative for coastal bathymetry monitoring Bergsma et al., (2019, 2021), at local, regional or even global scales (Almar et al., 2022). In general, there are three bathymetry measurement approaches: 1) radiative transfer (color methods), 2) under-water photogrammetry, and 3) wave kinematics inversion (S2Shores - CNES/IRD/SHOM). Here, we focus mostly on the wave kinematics and in addition a color method for the shallowest waters.

Color methods like Stump et al., (2003), are a powerful approach that linearly links a log-ratio of the green and blue band to a water depth.. While powerful, it does rely on in-situ knowledge of the water depth, but then spatially sparse depth measurements can be spatially extended to a full satellite scene (for Sentinel 2, 100x100 km). To overcome the reliance on local knowledge S2Shores depth estimates enable shallow water extension with the Stumpf method. In addition, Frugier et al, (2025) has developed an approach on wave breaking patterns from satellite images, linked to a breaker depth to generate depth estimates to calibrate the Stumpf-method. In the framework of SCOast-DT, a Monte-Carlo analysis, randomly sampling the known depths, enables a more stable final bathymetry with a model certainty measurement. Currently these are research configurations, and for full fusion, this thesis will include these approaches into the S2Shores framework.

On the other hand, we have wave-kinematic approaches. Individual wave-based bathymetry estimations from space are inherently noisy, preventing morphodynamic analysis with Satellite Derived Bathymetry only. This noisy nature is mostly linked to the sensor configuration, spatial resolution and capacity to detect moving objects (time-delays between detector-bands or sequential images, or video framerate). There is a clear distinction between global missions like Sentinel 1 and 2, that cover the globe but have lower resolution in comparison to (often commercial) agile missions like Pléiades (Néo), or CO3D, or commercial radar platforms that acquire imagery covering a smaller area but with more spatio-temporal information. For example, Sentinel 2 has a sparse spatial resolution but frames that are collected with a second delay limiting the detection of the wave displacement for smaller waves (with periods < 7 seconds), while Pléiades has very high resolution but a sampling interval of 7-8 seconds at best, introducing a wave displacement ambiguity. Novel matricial missions like CO3D, have a limited footprint but the capacity to acquire space videos at high resolution, removing any of the above spatial-temporal limitations. While optical imagery is widely used for coastal bathymetry, there are places in the world that are quasi-impossible to monitor given the high probability of cloud cover. Therefore, we extend our known S2Shores methods to Sentinel-1 to enable bathymetry estimation in cloudy environments. 

To overcome the noisy nature, sparse, irregular sampling of coastal areas, we propose an irregular opportunistic waves-based multi-sensor fusion approach to estimate bathymetry. This includes further development of S2Shores to add the capacity to estimate bathymetry using multiple methods and add intrinsic uncertainty estimations to finally enable fusion into a single optimised bathymetry with an associated uncertainty. In line with the current efforts at CNES (DTN/CD /LOT) to generate an ocean wave simulator including nearshore bathymetry, here we base ourselves on the ocean wave simulator using the Coastal and Regional Ocean Community model (CROCO) numerical model and sun-ray tracing to generate synthetic scenes. The development of the above fusion approach will be executed on this synthetic imagery at first to fully control our development environment.

The thesis consists of four key components: reviewing and evaluating existing methods, developing new generation fusible agile methods for several sensors, estimating intrinsic uncertainty for method fusion, and conceptualizing a pathway towards a new global atlas of bathymetric data. Looking ahead, the integration of morphological evolution, using Kalman filters, and multimethod fusion like WKB + spectral (color) analysis, hold promise for increasing the spatio-temporal resolution of bathymetric maps and increasing their precision and accuracy in the coastal zone, enabling morphodynamic analyses.

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For more Information about the topics and the co-financial partner (found by the lab!); contact Directeur de thèse - rafael.almar@ird.fr

Then, prepare a resume, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online before March 13th, 2026 Midnight Paris time!

• Master’s degree in Oceanography, Coastal Engineering/Science, Mathematics or a related field •Strong foundation in physical oceanography, coastal processes, or computational sciences   Technical Skills: 

• Remote sensing and satellite data analysis (e.g., Sentinel, Pleiades) 

• Strong programming skills for data processing and analysis (Python). 

• Strong statistical and data analysis skills, including wave analysis.   Attributes: 

• Ability to synthesize large datasets from different sources 

• Good scientific writing and communication skills. 

• Self-motivated, organized, and capable of working independently and collaboratively.