# Satellite-based flood mapping of coastal floods: The Senegal River estuary study case

E.T. Mendoza, E. Salameh, Imen Turki, Julien Deloffre, Benoît B. Laignel

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E.T. Mendoza, E. Salameh, Imen Turki, Julien Deloffre, Benoît B. Laignel. Satellite-based flood mapping of coastal floods: The Senegal River estuary study case. International Journal of Applied Earth Observation and Geoinformation, 2025, 138, pp.104476. 10.1016/j.jag.2025.104476. hal-05000938

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Satellite-based flood mapping of coastal floods: The Senegal River estuary study case ⭐

E.T. Mendoza<sup>a,b,*</sup>, E. Salameh<sup>a</sup>, E.I. Turki<sup>a</sup>, J. Deloffre<sup>a</sup>, B. Laignel<sup>a</sup>

ARTICLE INFO

Keywords:

Flood
St Louis
Senegal River
Satellite
Land type
Senegal

A B S T R A C T

This study employs an integrated approach, combining remote sensing and numerical modelling techniques, to characterize flood-prone regions resulting from the combined effects of extreme river water elevations and long-term sea-level rise in the Senegal River Estuary. Four different case scenarios of hydrodynamic conditions have been investigated to provide a quantitative assessment of flooding. Simultaneously, a Land Type classification using machine learning techniques has been conducted. Subsequently, the resulting land type map has been integrated with flood mapping simulations obtaining the different land types impacted by flood dynamics. The analysis shows that the buildings classification is the most impacted followed by vegetation and roads. This study highlights the flood-affected areas at a district level, offering relevant understanding for the development of effective adaptation strategies, disaster planning, adjusting policies with scientific knowledge, and supporting adaptive governance in the Senegal River Estuary.

# 1. Introduction

Coastal riverine areas are increasingly threatened by floods, primarily driven by extreme weather events. These floods pose significant risks, accounting for up to 80 % of fatalities along 100 km of the coastline (Hu et al., 2018) making these areas vulnerable to such impacts. This vulnerability is expected to increase due to the expansion of human settlements (Andreadis et al., 2022) and climate change. Projections indicate a rise in extreme flooding events in various regions, notably in Asia and Africa (Merz et al., 2021) and further aggravated given the sea level rise projections (Yang et al., 2015).

In West Africa, flood events have severely affected riverine infrastructure, causing major health and economic repercussions (Wagner et al., 2021). Notably, the Senegal River Estuary has experienced severe flooding events and only a few have been documented. In 2003, heavy rains caused the Senegal River to overflow, resulting in widespread flooding in Saint-Louis. As a precautionary measure, a man-made breaching in the sand spit known as Langue de Barbarie (Wade et al., 2007). This action resulted in an uncontrolled widening of the breach (Rey and Céline, 2017), leading to a complete change of the coastal and

river morpho dynamics of the estuary (Mietton et al., 2007; Ndour et al., 2018; Sadio et al., 2017; Anselme et al., 2010). In 2012, the city experienced another flood, resulting in significant damage and displacement of communities (Delbour et al., 2019) exacerbated by the region's low-lying status (Mendoza et al., 2023).

Given the significance of flood impacts, it is important to make accurate estimations of flood characteristics and spatial extent for flood risk analysis, particularly in regions like Africa, where most of the socioeconomic growth is concentrated in the coastal riverine zones. Precise flood mapping enables proactive measures to reduce the impacts of risks on communities and infrastructures and the development of early warning systems. However, assessing flood scenarios, particularly in developing countries, presents challenges due to limited data availability. Nonetheless, this limitation can be effectively overcome by integrating numerical modelling techniques and earth observation data.

Hydrological modelling plays a key role in assessing and forecasting flood impacts globally (Kumar et al., 2023). Among these, is the Hydrologic Engineering Center for River Analysis HEC-RAS (USACE, 2001), which integrates hydraulic data with geographical information systems (GIS) tools. It has been widely used to delineate flood plains

<footnote><sup>a</sup> Univ Rouen Normandie, UNICAEN, CNRS, M2C UMR 6143, 76000 Rouen, France</footnote>

<footnote><sup>b</sup> Cerema, RHITME ResearchTeam, Margny Les Compiegne, France</footnote>

<footnote>* This article is part of a special issue entitled: 'African EO ? Mapping - Invited' published in International Journal of Applied Earth Observation and Geoinformation.</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: ernesto.tonatiuh@univ-rouen.fr, ernesto.mendoza-ponce@cerema.fr (E.T. Mendoza).</footnote>
(Desalegn and Mulu, 2021; Tamiru and Wagari, 2022; Yang et al., 2006; Namara et al., 2022) and simulate water level variations in the Senegal River (Diedhiou et al., 2020).

In addition to hydrological modelling, land type classification is crucial for vulnerability assessments mapping (Nguyen and Liou, 2019; Liou et al., 2017; Talukdar et al., 2020). Satellite-derived Land type mapping enhances the ability for assessing flood impacts in changing landscapes (Petchprayoon et al., 2010; Priyatna et al., 2023). This mapping is especially important in developing countries given the fast population growth and economic development.

This study provides an initial assessment of flood-prone areas in the Senegal River Estuary. The work is organized into three key steps. First satellite imagery was employed for land-type classification through a supervised machine-learning approach. Next extreme river water elevations were analyzed under various sea level rise scenarios using satellite-derived data and numerical modelling techniques. Finally, the flood scenarios were integrated with the land type classification to quantify the percentages of different land types affected by each flood scenario.

## 2. Study site

The Senegal River is one of the longest in Western Africa, with a catchment area of approximately 340,000 km². The annual mean water discharge measured at Bakel station (Fig. 1A) River Flow varies from 10 m³/s to 3320 m³/s, being the average rate of 676 m³/s (Diedhiou et al., 2020; Kane and Niang, 2007). The Senegal River Estuary (Fig. 1A, 1B) is heavily influenced by Diama dam which is approximately 20 km upstream St. Louis (Fig. 1B) which was constructed in the mid 80's to stop the saltwater intrusion and to manage the discharge of the river during the rainy season which occur from July to October and improve

irrigation for agricultural purposes (Duvail and Hamerlynck, 2003; Barusseau et al., 1998).

The estuarine system of the Senegal River is located in the lower delta of the river (Fig. 1C), bordering the Atlantic Ocean approximately 250 km north of Dakar. The tidal regime is semi-diurnal and micro-tidal: 0.5 m at neap tides and 1.6 m at spring tides (Ndour et al., 2018). There are currently no available datasets for storm surge. However, satellite altimetry measurements indicate that storm surge effects are negligible, with values recorded below 0.1 m (Cissé et al., 2022; Mendoza et al., 2023). The delta faces many environmental problems, the most worrying being coastal erosion and recurrent flooding mostly during the rainy season. This area is highly urbanized with the presence of the Saint Louis City (~300,000 inhabitants) including a UNESCO world heritage site, tourist facilities, fishing activity and a national park of high environmental value. The Saint-Louis coastline is among the most vulnerable to flooding and erosion risks like many regions along West Africa (Mendoza et al., 2023). The area has a flat topography, with most neighborhoods built less than 2 m above sea level, sandy soil, and is dominated by surrounding water—conditions that expose the population to recurring floods from both river and marine sources (Delbour et al., 2019; Durand et al., 2010).

From a geomorphological perspective, the Senegal River delta is a classic illustration of a wave influenced delta (Bhattacharya and Giosan, 2003; Galloway, 1975). The Senegal coast presents three main wave regimes based on the season: (1) the highest waves are the year-round northwest swells, strongest from November to June; (2) wind-driven waves from Northern and Southern Hemisphere trade winds; (3) southwest swells from July to October, peaking in August and September (Samou et al., 2023). The strong southward longshore transport is exhibited with a long narrow sand spit (Langue de Barbarie) presently fronting the delta plain (Anthony, 2015; Sadio et al., 2017).

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** (A) Study area location: Senegal is situated in Western Africa south of Mauritania (B) The Senegal estuary is situated on the northern coast of Senegal (C) The Senegal estuary is composed of St. Louis Island which is fronted by the Langue de Barbarie and to the east Sor Island.</figcaption></figure>
The Langue de Barbarie coastline is about 30 km long, from the border with Mauritania (in the North) to the south of the village of Gnyam (locality of the Senegal River mouth). Due to a strong hydro-sedimentary environment dominated by the strong northwest swells, the sand spit is one of the most dynamic in the world. This spit has historically played an important role not only in the protection of the lower Senegal delta plain including the Ile de Saint-Louis and Ile de Sor, but also in regulating saltwater intrusion by diverting the mouth of the river several kilometers southwards (Sadio et al., 2017). Over the last century, the sand spit has advanced southwards with a migration rate ranging from 160 m/year to 870 m/year.

# 3. Datasets and methods

Datasets.

Satellite Imagery for Land Type Classification and Stereo Imagery to Obtain the Digital Elevation Model (DEM).

The Pleiades satellites are equipped with a High-Resolution Imager, which provides imagery in four multispectral bands (blue, green, red, and near-infrared (NIR) at a 2 m resolution, as well as a panchromatic (PA) band at a 0.5 m resolution. A standout feature of the Pleiades satellites is their ability to perform stereo and tristereo acquisitions, which makes it possible to generate Digital Elevation Models (DEMs). Pleiades images are accessible through the DINAMIS program (https://dinamis.data-terra.org).

Bathymetry.

Bathymetry data were obtained by manually digitizing local bathymetric maps from NAVIONICS, with a scale of 1:10,000, using the WGS 84 – UTM zone 28 N projection.

Water Levels.

Water level data were obtained from a gauge located on St. Louis Island, with daily readings recorded from 1964 to 2018.

Fig. 2 presents a workflow diagram of the methodology followed. First, satellite imagery was used to classify land types through supervised machine-learning techniques. Next, extreme river water levels were assessed under various sea level rise scenarios using numerical modeling and satellite data. Finally, flood impact scenarios were overlaid with land classification results to quantify the proportion of each

land type affected by flooding.

## 3.1. Land type classification

The land type was obtained from the same multispectral image acquired by Pleiades in March 2021 through a series of steps executed using the OrfeoToolbox and QGIS. First, the image was subject to a series of steps from the orthorectification, to the resampling, and the panch sharpening. The orthorectification was used for correcting geometric distortions and providing a georeferenced image, for which the 4 MS bands (R, G, B, and NIR), were fused to the higher resolution panchromatic band in a panch-sharpening process that enhanced their spatial resolution (from 2 m to 0.7 m). Zonal statistics were computed for each segment, including mean and std values of spectral intensities across the MS bands. Manual sampling was conducted for the following land type classes: buildings, roads, dry sand, wet sand, bare land, vegetation, mangrove, trees, and water. This manual sampling was used as training data for a Support Vector Machine SVM- (Cortes and Vapnik, 1995) classification model that was applied to classify the land type of each segment based on the zonal statistics, culminating in the creation of a detailed high-resolution land type map. The SVM algorithm was chosen for its well-established performance (Wieland and Pittore, 2014) in urban land cover classification using multispectral sensors (Soni et al., 2022; Zylshal et al., 2016).

Given the high resolution of the images, we first conducted a comprehensive analysis of the Senegal Estuary as a whole, followed by a focused examination of its three main land components: Langue de Barbarie, St. Louis Island, and Sor Island. This approach enabled a detailed assessment of the specific characteristics of each land type.

## 3.2. Modeling flood-prone scenarios

To determine flood-prone areas, this study has explored the numerical simulations provided by the Hydrodynamic-Erosion-River and Reservoir Simulation model (HEC-RAS) 2D version 6.5. The main purpose of this model is to calculate the elevation of surface water in the river across the floodplain. The model involves three modules: (i) RAS pre-processing, (ii) HEC-RAS modelling, and (iii) RAS post-processing.

<figure><img src="image_5.png" /><figcaption>Fig. 2. Workflow diagram of the used methodology.</figcaption></figure>
This model has been used in previous works for assessing flooding (Peker et al., 2024; Vashist and Singh, 2023) in different riverine environments combined with satellite imagery in Indus River (Afzal et al., 2022) and artificial intelligence techniques in Baro River (Tamiru and Wagari, 2022).

The first module integrates the transition of the Digital Elevation Model (DEM) to a Terrain Triangular Irregular Network (TIN). The DEM was derived from the Pleiades satellites obtained with high-resolution stereo images with a 2 m resolution and a panchromatic image of 0.5 m and completed with the bathymetry data that was manually digitized from bathymetry maps (Fig. 3A).

The second module involves implementing the 2D model with a 30 x 60 m grid (Fig. 3B), using the unsteady flow mode to generate the depth and flood-prone areas for four different case scenarios.

One of the most difficult aspects of flood studies is acquiring reliable water flow data. Although there are studies that use hydrological information along the Senegal River, this data was obtained at the Bakel station, located 560 km away from St. Louis (Bruckmann et al., 2022). In the case of the Senegal river estuary, this study relied on the water level data available for the site located in St Louis Island. Despite the absence of direct flow rate measurements, water level data provides an alternative for characterizing the hydraulic behavior of the river system. In this case, the HEC-RAS model can still simulate river dynamics and flood behavior by translating water level data into flow rates, providing a robust hydraulic connectivity assessment.

The annual maximum water levels observed over the 1964—2018 period exhibit a central tendency, with a mean of 1.46 m and a slightly lower median of 1.41 m, suggesting a mild right skew. This skewness hints at a few higher-than-average peaks, as reflected by the maximum value of 2.35 m. The data spans a wide range of 1.4 m (from 0.95 to 2.35 m), showing significant variability in maximum levels. The standard deviation of 0.34 m further supports this variability, indicating that individual values often deviate from the mean by a considerable amount. Table 1 presents four cases, each representing different combinations of environmental factors affecting the estuary.

In Case 1, we used the highest elevation (2.3 m) obtained from the daily chronological series of water level measurements recorded at a station on Saint-Louis Island, covering the period from 1964 to 2018. In this work, we refer to this elevation as the River Extreme Water Level

<caption>**Table 1**</caption>

Case studies and variables. REWL (River Extreme Water Level): 2.3 m. SLR (Sea Level Rise) based on the RCP 8.5 scenario for the lower confidence: 0.6 m, and higher confidence: 1.1 m. ESWLE (Extreme Sea Water Level Elevation): 2.1 m.

<table><thead><tr><th></th><th>REWL (m)</th><th>SLR (m)</th><th>ESWLE (m)</th></tr></thead><tbody><tr><td>Case 1</td><td>2.3</td><td>—</td><td>—</td></tr><tr><td>Case 2</td><td>2.3</td><td>0.6</td><td>—</td></tr><tr><td>Case 3</td><td>2.3</td><td>1.1</td><td>—</td></tr><tr><td>Case 4</td><td>2.3</td><td>1.1</td><td>2.1</td></tr></tbody></table>

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Configuration of the 2D HEC-RAS model containing (A) the digital elevation model, (B) computational mesh and boundary lines.</figcaption></figure>
(REWL).

In Case 2, we integrated a sea level rise projection of 0.6 m (lower
confidence interval), to evaluate the influence of climate change on
marine and river floods by the year 2100, based on the RCP 8.5 scenario,
in addition to the REWL.

Case 3 adopts a 1.1 m scenario (upper confidence interval) for the
same RCP 8.5 scenario. in addition to the REWL.

Finally, Case 4 considers a 1.2 m sea-level rise, includes the REWL, and incorporates an extreme sea water level elevation (storm surge, tide, waves) of 2.1 m. This scenario is referred to as the worst-case scenario in this work.

We selected these thresholds because the study aims to provide an
overview of water distribution in response to extreme scenarios,
excluding moderate cases to focus on more impactful conditions.

In all cases, water elevation was used in the upstream boundary
condition and a zero water elevation for the downstream boundary
condition except Case 4 in which we used a constant value for the storm
surge elevation.

The RAS module of post-processing involves the reverse process from
HEC-RAS back to ArcGIS. Once the different scenarios of the HEC-RAS
model were obtained, the data was exported to a GIS work environ-
ment (e.g. shapefile format).

3.3. Flood mapping scenarios integrating land type

In this section, we aimed to integrate the information obtained from
the flood-prone area modelization and land type classification. By
integrating these two components, we provide a complete interpretation
of flood impacts, detecting their spatial distribution across different land
types within the study area.

This integrated approach enabled a high-resolution assessment of the extent to which each land type was susceptible to flood inundation under varying sea level rise scenarios. By quantifying the percentage of land type affected within each flood case scenario, we obtained a fine distinction of land type vulnerabilities for the different districts within the study area. This information is useful to understand the complex relation between natural hazards and urban landscapes.

4. Results

4.1. Land type classification

Considering the Senegal Estuary as a whole, buildings represent the largest class with a coverage of 31 %, followed by bare land at 26 %, and sand (both dry and wet) at 19 %. Plants (trees, mangroves, and vegetation) account for 17 %, while roads constitute 9 %. Considering separately the three areas that constitute the Senegal Estuary, Langue de Barbarie presents coverage of sand (wet and dry) of 39 %, which is expected due to the nature of the sand spit system that constitutes this zone. Buildings have the next highest percentage at 20 %, while plants (trees, mangroves, and vegetation) cover a similar area at 19 %. In St. Louis and Sor islands, the building class represents almost half of the coverage (41 % and 42 % respectively). In the case of St. Louis, it is heavily covered with roads (24 %), indicating a highly urbanized area (31 %). For Sor Island, we can observe that the northern section is densely urbanized, while the southern sector is dominated by Bare land and plants (trees, mangroves, and vegetation) as the main percentage coverage components (Fig. 4).

4.2. Modelling flood case scenarios

In terms of flood potential, the extension of the flood in Case 1 was
1.1 km². In this scenario, the most affected areas were the narrow
portion of the spit alongside the river in Langue de Barbarie, the
Northern section of St. Louis Island, and the Northern outer rims of the
Island of Sor (Fig. 5A). When considering Case 2, the affected areas
remained mostly the same with a very small increment to 1.15 km². As
for the impact of Case 3, the extension increased to 1.75 km², the same
areas with a higher water elevation. (Fig. 5C). In the worst-case scenario,
the area increased to 4.1 km², raising the water levels in all three studied
areas, further impacting the northern section and also the southern
sections in both St. Louis and Sor Islands (Fig. 5D).

4.3. Flood mapping scenarios integrating land type

The integration of the flood impact and the land type was conducted
using each element that constitutes the Senegal Estuary separately
(Langue de Barbarie, St. Louis Island and Sor Island) considering each
case scenario. Fig. 6A illustrates the mapping of flooded areas for the

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** (A) Land type mapping for the Senegal Estuary. (B) detail of the classification in the St. Louis Island (C) percentages of the classification for the estuary, Langue de Barbarie, St. Louis Island and Sor Island.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Fig. 5. Flood-prone areas in the Senegal Estuary, (A) Case 1, (B) Case 2, (C) Case 3, and (D) Case 4.</figcaption></figure>

worst-case scenario in Lange de Barbarie. It highlights the narrowest section of the sand spit which is the most affected and is likely to experience a breach. In terms of land type affectation, the buildings category experienced the most significant impacts across all four cases. In Cases 1, 2 and 3, both buildings and the roads were impacted to a lesser extent (between 2% and 4%). Case 4 showed a higher increase in percentages, with the highest impact observed in the buildings and roads categories –14% and 10% respectively- (Fig. 6B).

Fig. 7A provides a detailed illustration of land type distribution and flood-prone areas for Case 4 on St. Louis Island. Predominantly, flooded regions will be concentrated in the northern district, the western side, and the southern riverbanks. Regarding the affected land types, buildings, trees, and roads experienced the most significant impact across all scenarios. In the initial three cases, percentages ranged around 10%, while in the worst-case scenario, the percentages for buildings and roads doubled (21% and 14% respectively).

When mapping the flood-prone areas using Case 4 on Sor Island, the district of Bayal, will be affected along the riverbanks. Notably, the districts of Corniche, Balacoss, Diamguene, HLM, and Leona will experience flooding inner land (Fig. 8A). If we dismiss the bare land classification, the buildings and roads will be the most affected across the four different scenarios considered.

## 5. Discussion

While most flood studies along the Senegal River and Estuary have mainly relied on remote sensing techniques (Bruckmann et al., 2022; Cissé et al., 2022; Delbour et al., 2019; Sandholt et al., 2003). Diedhiou et al. (2020) utilized the HEC-RAS model to investigate flood mapping scenarios along the Senegal River, highlighting a maximum water level of 4.5 m at the St. Louis Island site.

It is important to acknowledge the limitations of our study. In the case of the model used, although the provided DEM was high-resolution, the coarse model grid may have limited its accuracy, particularly in capturing small-scale features such as roads and buildings. Additionally, the model outcomes may have been influenced by simplified boundary

conditions: river water levels were used instead of water flow, and sea-originating water levels were applied as a constant value, with no fluctuations considered. The outcome of this study was not validated, primarily due to the lack of available data for comparison. As this is the first study of its kind in the area, baseline data for validation were not accessible. Despite the absence of formal validation, the maximum water levels predicted by the model (4.3 m under the worst-case scenario) closely align with those reported by Diedhiou et al. (2020).

Direct comparison between studies is difficult due to various methodological differences. Our analysis assumes that the 4.5-meter elevation reported by Diedhiou et al. (2020) was derived from the maximum 2000 m³/s flow rate scenario and that their methodology did not incorporate sea-level variations or provide explicit details regarding the spatial resolution of the DEM used.

This alignment provides confidence in the model's performance for this initial assessment, with future studies potentially incorporating validation as more data becomes available.

Regarding the study area, the incomplete representation of Sor Island resulted from constraints in the satellite stereoscopic image. Therefore, Sor Island is inadequately represented in the DEM used as input for the HEC-RAS model. Despite this limitation, the findings of our study have significant implications for flood management.

The evaluation of machine learning algorithms for object-based classification of urban areas, conducted by Wieland and Pittore (2014), provided valuable insights into the performance of various methods using medium and high-resolution multispectral imagery. While further developments have continued to explore object-based classification approaches in recent studies (Balha et al., 2021; Li et al., 2024), the results of Wieland and Pittore (2014) showed that support vector machine (SVM) and random trees algorithms perform better than other machine learning algorithms with SVM being the best in terms of transferability. Though transferability was not an issue for this study, as it focused on a single site using only one Pléiades image, the SVM algorithm was chosen due to its proven performance. The obtained results indicated a satisfactory level of reliability (Fig. 4B). However, our results have not undergone a thorough validation procedure. Despite the lack of
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** (A) Example of Langue de Barbarie flood-prone areas Case 4 (worst-case scenario) with Land type elements. (B) Percentage of affected land type elements for each case.</figcaption></figure>

proper validation, our study represents a significant advancement over existing land use maps of the area (Delbour et al., 2019), laying the base for more accurate land type classification in future studies.

A close analysis of the flood path preference reveals that the main affected areas are concentrated in low-lying lands, roadways, and the connectivity points between these elements. Floodwaters tend to accumulate in lower elevations due to natural drainage patterns, while roads, situated alongside these areas, serve as channels that facilitate the spread of water across the landscape allowing floodwater to travel more extensively and impact both low lying residential areas and transportation network.

The most affected area in terms of flooding was the Island of Sor, with buildings facing the greatest impact, followed closely by roads. These observed patterns of impact underscore critical management concerns. From a management perspective, the flooding patterns on the Island of Sor reveal critical vulnerabilities in infrastructure and urban planning, highlighting the districts that require prioritized intervention. The concentration of flood impact on buildings and roads, particularly in low-lying zones, points to the need for reinforced flood defenses and strategic urban planning that considers natural drainage paths. Local authorities must evaluate the existing infrastructure, particularly road networks, as they play a dual role—serving as critical access routes and as conduits that spread floodwaters. Protecting residential buildings and ensuring resilient transportation networks are essential not only to safeguard assets but also to maintain essential connectivity during floods. Potential investment on flood resistant barriers, drainage

upgrades, and strategic zoning in these vulnerable regions could significantly reduce future flood impact, protecting both public safety and infrastructure resilience.

The detailed flood mapping provided in this work delivers practical information and displays inundation extents under different scenarios. By combining HEC-RAS modeling with land type classification, our study identifies specific districts in Senegal that will be impacted by floods, offering crucial insights for disaster preparedness planning. Similar approaches have demonstrated success in enhancing flood prediction accuracy by integrating HEC-RAS with advanced machine learning techniques, which improves model robustness under evolving climatic conditions (Qureshi et al., 2025).

It is imperative to understand the future impacts of extremes, including the combination of different elements such as future sea level rise and storm surges, to develop adaptive measures. By addressing this environmental uncertainty, this flood mapping addresses the lack of this type of information in Senegal (Young et al., 2019) and highlights the specific implications of flood mapping for governance at an urban scale (Dordi et al., 2022; Meerow et al., 2024; Mietton et al., 2007). This complete understanding of climate change impacts can inform policy and decision-makers with the essential knowledge to formulate effective long-term resilience strategies in the Senegal River Estuary to support the development of adaptive governance frameworks (Christopher et al., 2024; Silver et al., 2013).
<figure><img src="image_10.png" /><figcaption>Fig. 7. (A) Example of St. Louis Island flood-prone areas Case 4 with Land type elements. (B) Percentage of affected land type elements for each case.</figcaption></figure>

**6. Conclusions and future Directions**

The most severe scenario resulted in 4.1 km² of flooding, primarily affecting Sor Island in its northern section, followed by St. Louis Island, where both the northern and southern sections were impacted, and finally Lange de Barbarie, particularly in the narrow portion of the sand spit. According to Land Type Classification, buildings were found to be the dominant land type across the Senegal Estuary, followed by bare land and sand. Langue de Barbarie primarily consists of sand, while St. Louis and Sor Islands are heavily urbanized, with buildings representing almost half of the land coverage. The analysis shows that the buildings classification is the most impacted followed by vegetation and roads.

The integration of flood impact and land type revealed that buildings experienced the most significant impact across all scenarios, followed by roads. Flood-prone areas were identified in specific districts of each island, with varying degrees of impact on different land types.

By considering these conclusions, stakeholders can better understand flood vulnerability and obtain crucial information to prioritize mitigation efforts, develop effective strategies for disaster preparedness and long-term resilience strategies in the Senegal river estuary. To be effective, these strategies must take a holistic approach, integrating socio-economic factors (Cissé et al., 2024), hydraulics (Chilton et al., 2021), sediment transport (Sarr et al., 2024), and ecosystem restoration (Wainger et al., 2017).

Future research in this area should focus on refining modelling approaches, improving data collection methods, and exploring additional factors influencing flood dynamics. Future land type classification could explore alternative machine learning algorithms, improve classification methodologies, and incorporate a validation utilizing ground truth data for land type. Finally, we recommend the integration of socio-economic elements to improve the utility for this type of study.

**CRediT authorship contribution statement**

**E.T. Mendoza:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization.
**E. Salameh:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization.
**E.I. Turki:** Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization.
**J. Deloffre:** Writing – review & editing, Writing – original draft, Methodology, Conceptualization.
**B. Laignel:** Funding acquisition.

**Funding**

This work was supported by the CNES in the framework of the Space for Climate Observatory program, within the Project Outils et indicateurs croisés multi-Satellitaires et Socio-économiques pour le suivi des aléas et risques des environnements littoraux et renforcer leur résilience
<figure><img src="image_11.png" /><figcaption>Fig. 8. (A) Sor Island flood-prone areas Case 4 with Land type elements. (B) Percentage of affected land type elements for each case.</figcaption></figure>

face au changement climatique. Exemple du littoral de Saint Louis au Sénégal. (Acronyme: OSS St Louis). The first author was funded for a postdoc position under the same project. This research was also supported by the International Space Science Institute (ISSI) in Bern, through ISSI International Team project #23–598 Coastal Resilience using satellites: CRESTE. The first author would like to thank Sergio Carriedo González for his invaluable support.

During the preparation of this work the author(s) used Chat GPT in order to check typos and grammatical errors. After using this tool/service, the author(s) reviewed and edited the content as needed and take (s) full responsibility for the content of the publication.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [E. Tonatiuh Mendoza reports financial support was provided by French Space Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper].

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jag.2025.104476.

## Data availability

Data will be made available on request.

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