**1. Introduction**

Mapping coastal areas is essential to tackle a broad range of environmental and social issues [1–3]. Therefore, a wide variety of scientific research disciplines could benefit from a better knowledge of this interface, especially regarding the monitoring and protection of coral reefs in archipelagos or the production of navigational charts [4].

Numerous reliable and accurate techniques exist to acquire bathymetric soundings. Data are often obtained through marine surveys equipped with multibeam or single-beam echosounders [5]. However, these approaches are usually impracticable in remote and shallow areas as well as time consuming and limited in terms of spatial coverage, and therefore remain costly. As an alternative, remote sensing is increasingly used to retrieve coastal bathymetry. Airborne data acquired with bathymetric LiDAR are useful to map larger areas but remain costly and limited spatially [6,7].

Over the past few years, satellite-derived bathymetry (SDB) has been increasingly used as it offers a more affordable and time saving alternative. Scientific studies have demonstrated the possibility of obtaining reliable bathymetric data through hyperspectral and multispectral (MS) imagery at various spatial resolutions, due to a correlation between water depth and reflectance data [8–11]. Nevertheless, SDB mostly relies on passive imagery,

**Citation:** Le Quilleuc, A.; Collin, A.; Jasinski, M.F.; Devillers, R. Very High-Resolution Satellite-Derived Bathymetry and Habitat Mapping Using Pleiades-1 and ICESat-2. *Remote Sens.* **2022**, *14*, 133. https:// doi.org/10.3390/rs14010133

Academic Editors: Simona Niculescu, Junshi Xia and Dar Roberts

Received: 1 November 2021 Accepted: 22 December 2021 Published: 29 December 2021

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which strongly constrains its use to clear and shallow water areas [12,13]. Depth can be retrieved from satellite MS imagery using physics-based or empirical models. Physicsbased models rely on the physics or the radiative transfer of light in the water column and the physical properties of the water constituents that can be estimated with or without field measurements of depth for calibration. Some physics-based models are entirely based on the inversion of the radiative transfer model, such as WASI and BOMBER, but they can be complex to implement [14–16]. On the other hand, empirical models are limited by the need to calibrate the MS imagery with in situ measurements [17,18].

There is a real need for producing bathymetric data solely from satellite images. In this context, the launch of the NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) in September 2018 offered new prospects [19]. This satellite aims to monitor the cryosphere and terrestrial biosphere using the green 532 nm LiDAR with photon-counting capability. Pre-launch studies highlighted its potential to penetrate the upper part of the water column and reach the bottom [20]. A pioneer study has recently validated accurate ICESat-2 bathymetry retrieval at 38 m depth in very clear waters [21]. A second relevant study used ICESat-2 bathymetric measurements, down to 18 m depth, to calibrate and validate Sentinel-2 imagery at 10 m pixel size [17]. This spatial resolution nonetheless remains limiting for some applications (e.g., marine ecology, navigation).

Our paper aims to create a higher resolution digital depth model (DDM) by fusing active ICESat-2 bathymetric soundings and 0.5 m Pleiades-1 passive MS imagery in order to provide very high-resolution (VHR) satellite-based bathymetry and habitat maps of the coral reefscapes in Mayotte. First, a density-based algorithm was implemented on ICESAt-2 ATL03 L2 dataset to remove the noise in photon data and detect the water surface. The noise arises from several sources, including the laser pulse being scattered by the atmosphere, the solar background noise effects, and the detector dark noise. In our study, the main noise source is associated with photons that are scattered by particles in the water column [22]. Based on this first clustering, photons from the seabed were identified and corrected for the refraction effect occurring at the air-water interface. Producing bathymetric maps requires finding a function that describes the relationship between bathymetry measurements and the remotely sensed spectral values of the satellite image [8]. In this study, we used the band ratio model developed by [23]. First, we derived the above water surface reflectance log ratio of two spectral bands. Then, we characterized the relationship between the ratio and ICESat-2 water depth measurements [17]. Therefore, this study innovatively produces a VHR DDM and VHR benthic habitats map of the area from satellite data without a need for in situ measurements. Bathymetric data were used to remove the effect of the water column and generate a digital albedo model (DAM) to classify benthic habitats [24–27]. Finally, the vertical accuracy of the predicted depths was assessed by comparing the bathymetric data to the French naval hydrographic and oceanographic service SHOM bathymetric LiDAR and multibeam echosounder reference dataset (Litto3D®). Classification performances were evaluated using a confusion matrix.
