Challenges and Opportunities in Predicting Future Beach Evolution: A Review of Processes, Remote Sensing, and Modeling Approaches

Abstract

What are the main findings?

A comprehensive review identifies 39 multidisciplinary drivers of beach evolu-tion, spanning meteorological, oceanographic, geological, biological, and anthro-pogenic factors.

A case study of the Langue de Barbarie sandspit in Senegal (West Africa) demonstrates how integrating in situ measurements with satellite-derived information can reveal key processes that are often overlooked in coastal studies.

What is the implication of the main finding?

Improved prediction of shoreline evolution requires the combination of remote sensing observations, numerical models and local monitoring in order to capture the multiscale and multidisciplinary drivers of change.

Using high-resolution, long-term satellite data alongside in situ surveys provides a pathway toward more reliable, reproducible, and globally transferable approaches to coastal risk assessment and management.

This review synthesizes the current knowledge of the various natural and human-caused processes that influence the evolution of sandy beaches and explores ways to improve predictions. Short-term storm-driven dynamics have been extensively studied, but long-term changes remain poorly understood due to a limited grasp of non-wave drivers, outdated topo-bathymetric (land–sea continuum digital elevation model) data, and an absence of systematic uncertainty assessments. In this study, we classify and analyze the various drivers of beach change, including meteorological, oceanographic, geological, biological, and human influences, and we highlight their interactions across spatial and temporal scales. We place special emphasis on the role of remote sensing, detailing the capacities and limitations of optical, radar, lidar, unmanned aerial vehicle (UAV), video systems and satellite Earth observation for monitoring shoreline change, nearshore bathymetry (or seafloor), sediment dynamics, and ecosystem drivers. A case study from the Langue de Barbarie in Senegal, West Africa, illustrates the integration of in situ measurements, satellite observations, and modeling to identify local forcing factors. Based on this synthesis, we propose a structured framework for quantifying uncertainty that encompasses data, parameter, structural, and scenario uncertainties. We also outline ways to dynamically update nearshore bathymetry to improve predictive ability. Finally, we identify key challenges and opportunities for future coastal forecasting and emphasize the need for multi-sensor integration, hybrid modeling approaches, and holistic classifications that move beyond wave-only paradigms.

1. Introduction

Predicting the evolution of sandy beaches remains a significant challenge in coastal science. Although in situ measurements are the most accurate means of capturing coastal morphology (i.e., the shapes and features of coastal landforms such as beaches, dunes, cliffs, estuaries, and deltas) and dynamics [1], their geographical and temporal specificity restricts their applicability to broader contexts. Satellite Earth observation increasingly provides high-resolution, repeatable coverage on a global scale, complementing ground observations with multi-sensor perspectives and decades-long archives. Meanwhile, substantial progress has been made in morphodynamic modelling, particularly with the development of process-based tools such as XBeach anfd CROCO, which solve coupled equations of flow, wave propagation, sediment transport and bed updating [2,3]. However, these models are limited by the requirement for highly detailed input data (often at a resolution of a few metres) and typically diverge beyond short-term horizons of days to weeks. In order to reconcile these approaches, the complexity of beach systems must be revisited, the origins of model drift must be understood, and multidisciplinary drivers must be integrated in order to improve medium- to long-term prediction (months to years). Readers may wish to consult reference works [4,5,6,7,8,9] for a broader context.

Much of the research conducted on sandy beaches has focused on how they respond to storm events. Storm waves are a dominant factor in short-term morphological change [10,11]. However, global assessments show that only 24% of beaches worldwide are eroding, while 28% are accreting, and almost half remain stable despite ongoing sea-level rise [12], though such macro-scale statistics are inevitably constrained by factors such as spatial resolution, tidal or seasonal aliasing, and limited regional representativeness. Nevertheless, this observed diversity of results suggests that beach change cannot be explained by wave forcing and sea-level rise alone. Local sediment budgets, geological constraints and human interventions often govern shoreline trajectories. For instance, in northern France, the limited thickness of the sediment and the exposure of the underlying bedrock during erosion phases can render beaches effectively static, thereby decoupling them from hydrodynamic forcing. Similarly, offshore sandbar migration [13] or sand deficits caused by river damming [14,15] can influence the position of the shoreline independently of storm activity. At larger scales, global climate variability, such as the El Niño–Southern Oscillation, can induce synchronous coastal anomalies across regions [16].

Field studies from highly dynamic environments illustrate these complexities. For example, in Nha Trang, Vietnam, morphodynamics reflect the interplay of tropical storm activity, sediment supply and human intervention [17,18,19]. Similarly, at the Langue de Barbarie in Senegal, coastal dynamics are influenced by waves, river flows, channel openings and human modifications [20,21,22,23]. These examples demonstrate that non-hydrodynamic factors, such as geological, biological or anthropogenic processes, can dominate the determination of beach evolution, particularly at multi-annual or regional scales. The relative lack of such processes in current models reflects observational gaps and insufficient knowledge of their variability, rather than their irrelevance.

These gaps have begun to be addressed by remote sensing technologies. Shore-based video systems, unmanned aerial vehicles (UAVs), airborne lidar and, increasingly, satellites provide observations across scales and environments [24]. Long Landsat archives (approximately 40 years) and satellite altimetry archives (approximately 30 years) now enable retrospective analyses of shoreline dynamics and sea-level variability. New missions such as Sentinel-1/2/3, SWOT and ICESat-2 are expanding the scope of mapping to include waves, water levels, shoreline change and shallow bathymetry (typically sea floor depths of the nearshore area from 0 to 15 m [25,26,27,28]. Increasing revisit frequency and resolution, as well as methodological advances—including optical, radar and lidar approaches—now make it possible to extract nearshore morphological information at an unprecedented scale. However, challenges remain in resolving the land–sea interface, updating topo-bathymetry (i.e., land–sea continuum digital elevation model) dynamically, and integrating fragmented methods across diverse settings.

This paper provides a comprehensive review of the multidisciplinary factors that drive beach evolution, as well as the observation and modelling approaches used to monitor and predict these factors. Three themes are emphasised: (i) a systematic review of remote sensing methods and their applicability to beach monitoring, paying attention to uncertainties; (ii) satellite Earth observation as a foundation for dynamic coastal digital twins that can capture spatiotemporal variability from global to local scales; and (iii) developing hybrid, modular modelling frameworks that combine conceptual, process-based and Artificial Intelligence (AI)-driven approaches while explicitly incorporating uncertainty quantification. Using Langue de Barbarie in Senegal as a case study, we demonstrate how multidisciplinary drivers and data streams can be integrated in practice. By moving beyond wave-only paradigms and embedding uncertainty in forecasts, our aim is to make progress towards a more holistic, multi-scale and decision-relevant prediction of sandy beach evolution in the context of a changing climate and human pressures.

2. Scope and Literature Selection

This review summarises a wide range of literature that addresses the factors shaping the sedimentary dynamics of sandy beaches. The bibliography was curated to strike a balance between breadth and concision. Of the nearly 800 preselected articles, only 121 works published between 1960 and 2023 were retained. Recent synthetic publications integrating previous advances were favoured, though older foundational contributions providing enduring analytical insights predating current methods of satellite observation and numerical modelling were also included. This approach ensures that the historical context and current state of research are both adequately represented.

The complexity of sandy beach systems stems from their multifactorial nature, where geological, biological and anthropogenic elements interact with hydrodynamic forces. Sediments at a given site often comprise a mixture of ancient and modern particles that were deposited under different conditions. These mixed sediments can form sedimentary structures that influence local hydrodynamics, thereby feeding back into sediment reorganization. Consequently, beaches function as complex adaptive systems that are in a constant state of transformation. This intrinsic complexity has led to the development of numerous coastal classification systems, ranging from comprehensive frameworks that encompass all coast types (e.g., [29,30,31]), to more parsimonious models that are based on bulk hydrodynamic parameters and sediment grain size (e.g., [32,33,34]). The former capture the diversity of coastal settings, while the latter underpin much of contemporary morphodynamic modelling and yield realistic short-term results for relatively homogeneous, linear coasts. However, such simplified classifications fail to fully represent coastal dynamics in the many cases where additional processes intervene, such as geological constraints, biological mediation, human interventions or climate variability.

In order to bridge this gap, it is necessary to systematically identify the full range of local processes that influence sediment dynamics (see Figure 1). This provides a clear understanding of discrepancies between model representations and real-world conditions, helping to evaluate model performance and the sustainability of predictions over timescales ranging from months to decades. The review therefore proceeds in two steps: (i) a synthesis of the main drivers of sandy beach evolution, as extracted from the literature (Figure 1), and (ii) a structured description of these factors in the subsequent chapters. While it is impossible to be exhaustive given the diversity of environments, this framework aims to capture the most prevalent and globally relevant drivers without fragmenting into hundreds of subclasses. Similar processes that exert comparable effects on sediment dynamics are grouped together to maintain generality. Some exclusivity rules apply, such as cratonic settings precluding subsidence processes or mangroves and glacial erosion not being able to occur simultaneously, but these are rare. In most cases, beaches are influenced by several dozen interacting factors simultaneously. Figure 2 illustrates these interactions, showing how physical and biological parameters interrelate in shaping coastal evolution [31]. This complexity highlights the importance of multidisciplinary approaches combining in situ measurements, remote sensing observations and numerical modelling.

3. Review of the Diverse Factors Impacting Beach Dynamics

3.1. Oceanographic and Climatic Forcing

Sandy beaches comprise both an aerial component—bounded landward by the foot of dunes, vegetation, or engineered structures such as seawalls—and a submerged component extending through the surf and nearshore zones, down to the closure depth that varies between 8 and 30 m [35,36]. Globally, this zone is the most morphodynamically active, undergoing frequent and sometimes rapid changes in response to hydrodynamic forcing. Waves and tides dominate these processes, particularly in the surf zone, where they drive granulometric sorting, morphological reorganization, and sediment transport pathways [37]. The nearshore hydrodynamics of a beach can be summarized by five primary drivers (Figure 3): O1—gravity wave characteristics at different time scales (single waves, wave groups, and infragravity motions); O2—storm wave regimes; O3—temporal variations in water level due to tides; O4—seasonal upwelling; and O5—large-scale ocean circulation and steric effects.

Most studies on beach erosion and morphodynamic modeling concentrate on the influence of wave forcing, while other oceanographic processes remain underexplored. For example, upwelling currents—driven by morphological and hydrodynamic factors [38]—can introduce a strong seasonal signature to alongshore sediment transport, yet their role in beach morphodynamics is rarely documented. Riverine influences also contribute significantly, modifying coastal currents and salinity (H1) and altering local water levels (H2). Recent studies suggest that freshwater discharge can modulate coastal sea level at regional scales [39], although its direct influence on sandy-beach morphodynamics remains under-investigated at the local level [16].

Figure 3 provides an overview of the 39 factors considered in this review, organized into six categories: anthropogenic (A), biological (B), climatological (C), geological (G), hydrographic (H), and oceanographic (O). This framework highlights the multiplicity of drivers, and in particular the strong interactions between climate variability, hydrography, and oceanographic forcing.

Oceanographic processes are tightly linked to climatic drivers. Hydrodynamic extremes such as storm surges or wind waves are forced by atmospheric conditions, while large-scale modes including monsoons, typhoons, or ice melt further alter coastal hydrodynamics and sediment fluxes. Climate oscillations such as the El Niño–Southern Oscillation (ENSO) and related modes strongly influence coastal water levels, generating intra-seasonal to interannual shifts in the shoreline of tens of meters [40,41,42]. Periglacial coasts provide another example of climate-linked processes, where erosion is driven directly by permafrost thaw [43].

Six main classes of climate-related forcing can therefore be identified: C1—weather climatologies; C2—impacts of ice melt and local cryospheric processes; C3—extreme climatic phenomena (monsoon, hurricanes); C4—aeolian transport including sandstorms; C5—impacts of ENSO and interannual variability; and C6—long-term climate change including sea-level rise. The influence of these drivers is evident in case studies such as the 21 sandy beaches of Montevideo (Uruguay), where sea surface temperature anomalies (SSTA) modulate accretion–erosion cycles: higher SSTAs favor calm, accretionary conditions, while lower SSTAs correlate with high-energy winds and erosive conditions [44]. However, global responses remain difficult to generalize. Although only 24% of the world’s beaches are currently eroding [12}, local erosion can accelerate under combined forcing of sea-level rise and storm intensification, as observed on the central Chilean coast, where erosion rates have risen sharply due to (i) sea-level increases of up to 30 cm during ENSO warm phases and (ii) an increase in extreme storm frequency from ~5 events per year in the 1960s to more than 20 in recent decades [45].

3.2. Biological Effects on Beach Morphodynamic

Biological influences on beach dynamics are multifaceted and can be grouped into five main classes: B1—algae and seaweeds; B2—biodeposition and algal wrack; B3—reefs and bioherms (e.g., corals and Sabellaria spp.); B4—benthic colonies (e.g., oysters, Crepidula, cockles, mussels, and Lanice conchilega); and B5—beach vegetation (Crambe maritima; Abronia maritima; Atriplex leucophylla (Moq.); and Casuarina equisetifolia, or filao). These elements interact with hydrodynamic and sedimentary processes in complex ways, altering sediment stability, transport, and beach morphology (Figure 3).

Biodeposition (B2) plays a particularly important role in tropical settings, especially on coasts dominated by reefs or mangroves. Reefs (B3) are both a major supplier of biogenic sediments and a promoter of seagrass bed development, with seagrass leaves, rhizomes, and roots (B1) attenuating littoral currents, trapping sediments, and stabilizing substrates. Their dead leaves form mats that act as a protective layer against wave energy, reducing erosion and fostering dune accretion [46]. Posidonia wrack in the Mediterranean exemplifies this: accumulations of leaves and rhizomes can reach several meters in thickness, functioning as natural breakwaters that absorb storm-wave energy and protect beaches [47]. Even smaller wrack deposits and organic debris enhance dune formation and substrate cohesion [48].

Phases of reef decline (B3) are often linked to storm damage, interspecific competition, and human pressures [49]. Similar dynamics affect Sabellaria spinulosa reefs, where subtle variations in grain size or the loss of adjacent bioclast sources can trigger structural collapse [49]. Benthic colonies (B4) provide another clear linkage: in well-aerated environments, population density reflects sedimentary dynamics, with stronger hydrodynamics reducing faunal abundance [50]. Shell accumulations, common near shellfish beds or in channels, are partly natural lag deposits but may also include reworked older material from eroded beach rocks or river input. These shells, fragmented by wave action or biological activity (e.g., shell-crushing predators), alter sediment texture [30,51].

Biogeomorphic processes mediated by ecosystem engineers are particularly relevant. Species such as Lanice conchilega construct tubes that alter sediment sorting, creating coarser and better-sorted patches relative to surrounding areas [52]. Modeling studies show that at low population densities, tube-building reduces flow velocity and increases accretion, generating feedbacks that stabilize populations and modify beach profiles [53]. Such organisms, together with algae, impart strong seasonal variability to sediment dynamics that either amplifies or counterbalances storm-driven processes. Conversely, filter feeders enhance fine sediment deposition, as shown in Mytilus cultures where silt and organic matter concentrations are significantly elevated relative to control sites [54].

Beach vegetation (B5) also plays a direct role in shoreline stabilization and erosion control. For instance, Casuarina equisetifolia (filao) has been widely planted along the Senegalese coast from Dakar to Saint-Louis to stabilize dunes [55]. Other dune and beach plants (e.g., Crambe maritima and Atriplex leucophylla) contribute to sediment trapping, dune formation, and beach resilience.

Taken together, biological factors contribute to beach morphodynamics by modifying sediment stability, influencing grain size distribution, and introducing seasonal or long-term variability in transport rates. As illustrated in Figure 3, they interact with geological, oceanographic, and anthropogenic drivers, making biological processes indispensable to any holistic framework for beach evolution.

3.3. Geological Effects on Beach Morphodynamic

The field of geology related to rocks, sediments and geomorphology is the most complex to summarize and consider in models, because it is the result of local and regional phenomena that took place during the last 20 thousand years, combined with current phenomena. However, it is often summarized in models by the average grain size. Two other parameters are used in models to describe sediments, porosity and particle fall velocity, but they are generally left at a default value due to lack of knowledge of the local physical data. Such a simplification is possible in an environment where the sediment thickness is plurimetric and where all sediment particles are similar throughout the year. Such cases do exist, but they are rare and represent, for example, only a few percent of the French coasts. For all other cases, an analysis of the influence of geological factors must be carried out. Eleven stress factors are distinguished for geological aspects: G1—Subsidence and post-glacial rebound; G2—Beach rock; G3—Sediment availability (thickness and external contributions); G4—Sediment granularity (mean grain size, sorting, skewness, ...); G5—Shape (shell, shape, ...); G5—Shape (shells/rounded grains) of sediment grains; G6—Porosity, gas content and fall velocity; G7—Clay content; G8—Bedforms; G9—Hinterland slope; G10—Shoreline sinuosity; and G11—Effects of earthquakes and volcanic explosions.

3.4. Geological Settings and Sediment Availability

The characteristics of the coastal morphology, the sediments and their thickness, the phenomena of subsidence or isostatic rebound, and the importance of the sedimentary flows brought by the rivers, are all directly related to the nature of the local geological base, which is therefore essential because it leaves its mark on each of these different parameters. The control of deposition by sedimentary tectonics (G1) is the main force controlling the thickness of deposits in some regions [30]. This is particularly the case in northern America and northern Europe, where the disappearance of the Pleistocene Scandinavian ice sheet is at the origin of a subsidence that allows the deposition of a large thickness of sediments in the North Sea basin [56]. 28% of the world’s beaches are under accretion [12]. This phenomenon is favored at high latitudes, where uplift rebound from post-glaciation is most important [57]. For example, it occurs in parts of Scandinavia: the Baltic Sea, especially the Gulf of Bothnia in Finland, and Sweden show land uplift due to global isostatic adjustment at a sufficient rate that the projected global mean sea level rise may be completely offset, albeit at a lower rate [58,59]. Sediment availability may thus be the rule in certain regions of northern Europe, while in others, such as northwestern Spain or the northern half of France, it may be in deficit, making these regions more sensitive to anthropogenic impacts. On the other hand, subsidence (G1) is a negative ground movement that is becoming one of the major problems in coastal and delta cities worldwide [60]. Subsidence is the gradual or sudden lowering of the land surface due to consolidation of sediments, which causes subsurface movement of earth materials as a result of increasing effective stress. This is a major problem in some regions where subsidence is occurring faster than sea level rise. For example, subsidence affects the Mississippi Delta at very different rates, not only because of the vast size of the area, but also because of the different triggers. The age and characteristics of the sediments result in subsidence rates ranging from 2 mm/year to 16 mm/year; in industrial areas or where there is massive extraction of material from the subsurface, subsidence rates can reach 70 mm/year [61].

Beach Rocks (G2) are cemented beach sands characteristic of intertropical coastlines, formed by the deposition of calcite from groundwater and/or aragonite from seawater. The cement between the sand grains often comes from the water table, with secondary cementation from exposure, and they occur up to 2 m above sea level, but more commonly in the intertidal or nearshore zones [62]. This consolidation of sediments is a modern phenomenon, producing sandstones under the sand of a beach, containing marine shells or coral fragments and sometimes objects of contemporary civilization [63]. Beach rocks are quite common in microtidal environments; for example, 30 beaches along the Israeli coast have beach rocks that are subject to erosion, creating distinct morphological features as wave action and coastal currents remove the unconsolidated sediment that originally covered them [64]. The formation of beach rock can irreversibly alter the nature of the coastline, transforming sandy beaches into rocky foreshores with implications for their ecology. It can also limit the morphodynamics of the beach when the beach rock is covered by a few decimeters of sediment. Since carbonate cement precipitation depends on the temperature of the interstitial water of beach sediments, the rate of beach rock formation may change due to expected climate change, with significant impacts on the coastline [65]. The availability of sediment is critical. Sediment can come from the land or from the sea. Onshore transport results from the migration of ripples and bedforms from the inner shelf [15], which is thought to be an important source of beach sediment in several regions of the world. On the other hand, surficial sediment would slowly move down the slope of the seafloor due to its own gravity, and the steeper the slope, the faster and larger the volume of sediment would be transported over the long term [66]. The most important in this area are riverine inputs, which are the largest contributors to beaches [67,68]. Precipitation, land type and use, and watershed characteristics are critical in determining these inputs. At the coast, sediment is redistributed by waves and currents and can move within sediment cells, which can be defined as uninterrupted. Any alongshore gradient in alongshore sediment transport drives sediment imbalances and morphological changes.

3.5. Nearshore Sediment Thickness

Nearshore sediment thickness (G3) is critical for assessing the sediment availability of coastal systems in the presence of sediment dynamics, but it is largely unquantified due to the difficulty of conducting geological and geophysical surveys across the nearshore. Such a survey has been conducted at Fire Island, New York, to characterize sediment budgets and determine the geologic framework controls on coastal processes [69]. But in general, despite the lack of measures, sediment is estimated to be infinite without any data to confirm it, yet for many environments the bedrock is very close and rapidly outcrops. This is the case for cratons, which are stable parts of a continent composed of ancient crystalline basement rocks. Examples include the Armorican Massif of Brittany in France or a large part of the West African coasts, where subsidence is negligible; in these cases the sediments form a layer only a few meters thick, except at the level of partially filled paleovalleys and sandbanks [70]. Along these coasts, another exception is the bay of Mont-Saint-Michel, where the thickness of the sediments increases from 0–2 m at the entrance of the bay to 20 m near this island [71]. The thickness of the sediments is delineated using high-resolution seismic profiles and shallow vibracores, but these systems are very difficult to implement in shallow water and limit this knowledge to very few areas in the world. Nevertheless, knowledge of the distribution and geometry of geological features should be incorporated into conceptual and mathematical models of coastal evolution to improve predictions of coastal evolution [72]. The hydrographic conditions on the coast and on the shelf are very different, as are the sediments and their characteristics [30]. The small size of the grains, their similarity and the temporal stability of their morphology make it very difficult to measure the movement of sand grains in situ. Dating of these grains is also ineffective, making it very difficult to quantify sediment fluxes in a natural environment. The nature of the grains makes it possible in some cases to link the sands to nearby rock outcrops if these rocks have particular characteristics. In the vast majority of cases, however, the origin of beach sand remains generally undetermined and is linked to general principles: (1) Positive sediment supply causes beach accretion [73]. (2) In prograding coasts, most of the sand must be delivered by rivers [30]. (3) A minor source of sediment comes from the erosion of irregularities in the shoreline that extend into the sea. (4) In some environments, coastal sand is derived from the shoreward parts of the continental shelf [74].

3.6. Sediment Characteristics

Sands taken from the lower limit of the intertidal zone of 78 beaches along the French coast [75] range from fine sands (165 µm) to gravelly sands (3100 µm). If these averages can be used when the sediment is well sorted, this becomes erroneous in the presence of heterogeneous sediments. For this sediment granularity description (G4), Folk (1966) [76] describes the difficulty of representing sediments as follows: “No single parameter or combination of parameters (average, sorting (So), skewness (Sk), and the distribution of grain proportions around the mean, defined by kurtosis (K)) is adequate to reveal all the properties of a complex frequency distribution of a sediment; the entire granularity curve must be seen to be appreciated, just as no anthropologist can adequately characterize a Brigitte Bardot by four measurements alone”. If Friedman (1967) [77] proposed 7 parameters to describe a beach sediment, the 3 parameters median grain size (Md), sorting coefficient (So) and skewness (Sk) are classically used to study sediment dynamics [78,79]. This is the case, for example, for the description of the fluvial or aeolian origin of the sediments of the beach of the east coast of India (Chauhan, 1990) or for the characterization of the organization of the sedimentary cells on the adjacent to the Mekong delta [79]. The characteristics of the sediments (kurtosis, sorting and percentile of coarser grains) can allow the recognition of processes such as the impact of the monsoon on the variability of the granularity and the processes at the origin of the dynamics of coastal sediments [80]. Further refinement of these methods consists of coupling these grain size parameters with the geochemical characteristics of the sediments and applying them to each of the grain size phases. Sedimentary dynamics of sediments therefore tend to refine methods by using dozens of values applied to dozens of measurement points. In contrast, the morphodynamic modeling of beaches is based on the calculation of an average rate of fall, calculated from the average grain of an average beach sediment. However, there are numerous examples describing the variability of beach sediments on the surface and also a vertical bedding [30,81,82]. Another fact is that the mean grain size generally comes from a sample taken on the beach from the air, while the waves are moving the submerged sediments. Nevertheless, differences between the beach sediments and those of the transition zone and offshore have been described since Emery (1960) [83] with an increase in the proportion of silt and the systematization of the bedding in the transition zone. It should also be noted that the granularity is not stable in time, with a seasonal variability [20,82]. Modifications due to external sediment flows, from anthropogenic changes or from variations in fluvial discharges, can also play an important role in the composition of the banks and their stability [84].

Beach sands are often composed of mixtures of mineral grains from a variety of sources, although sands composed of a single mineral type such as quartz or carbonates also occur. Which minerals and grain sizes to expect depends on the geology of the source rocks or, in the case of bioclastic material, the species of carbonate-producing organisms [85]. The nature of the particle affects the grain specific gravity (G6), which is of 2.65 kg/m³ for quartz, which is the most common mineral of sands, but the specific gravity could be higher for other minerals such as mica, 2.8 to 3.2, and feldspar, which are common in sands of granitic coasts. Higher values are observed for pyroxene, with a specific gravity from 3.0 to 4.0, and iron silicates such as olivine from 3.2 to 4.5, which could be the main mineral of beaches near volcanoes (G7), beaches where light minerals are absent. The basalt generally contains some percent of magnetite, which presents the largest specific gravity of 5.2. Volcanic beach sands are one of the most widespread soil groups in many regions of the world and dominate the surface geology of many coastal areas such as the Pacific, Central and South America, Africa, and Asia [86]. The properties of these volcanic sands can also be studied using a geochemical approach, where the percentage of iron in the sediment is correlated with geotechnical parameters, especially the friction angle [87], which is important for modeling sediment dynamics. Specific gravity is less straightforward for marine carbonates because of the influence of biology on the internal structure of skeletons and the resulting porosity. For example, oyster and cockle shells give values close to quartz: from 2.33 to 2.60. However, coral aragonite has a lower specific gravity, from 1.78 to 2.94 [88]. The porosity will also be very different between sands composed of rounded grains of quartz, which will be more compact than particles coming from corals, which can present a variability in grain porosity from 33 to 70% [89]. This gives a bulk density of corals from 0.85 to 1.7.

The geometric characterization of a grain (G5), although it requires very sophisticated tools, is not very complicated in principle. Performed on a population sample, it provides a weight, size and diameter. The problem is more complicated for a non-spherical particle; in fact, if a single dimension is sufficient to characterize a rounded sand grain, two or three parameters will be useful for needle-shaped or flattened grains, and even more will be needed for a grain of any shape, such as shell debris [90]. Seafloor composition is in fact a synthesis involving the temporal and three-dimensional variability of seafloor characteristics, including but not limited to grain size distribution, stratigraphy, compaction, mineralogy, dissolved oxygen, and organic content [31]. There is a real difference in the specific gravity, porosity, and heterogeneity of the particles that make up the sediments of a beach from a nearby beach. If we take, for example, the coast near Dakar (Senegal), Riffault (1980) [91] observes that the average sands have a calcium carbonate content of 25% ±5 at Mbour, while 30 km further north, the beaches of Rufisque have medium sands with a calcium carbonate content of 80% ±10. This limestone comes from old shells that have been reworked and mixed with fine and medium sands. Thus, there can be a very significant difference in the sands of neighboring beaches that also have a comparable mean grain size, with the shelly sands of the north having a high proportion of fine sands, while the beaches of the south are essentially composed of medium sands with a low proportion of shells [91]. Thus, to make the morphodynamic models efficient, it would be necessary to replace the undeformable sphere of single diameter, hitherto represented by the mean grain size, with a population of grains described by a set of parameters representing the heterogeneity of size, physical properties and shape of the grains. It is probably in this domain of granular sediment properties that the largest gaps between reality and models exist.

Geotechnical properties of sediments and soil behavior (G6) are difficult to study in the energetic beach environment and few studies have been carried out there. This is the case, for example, for the interstitial fluid of intertidal sands, which systematically show interstitial gas at least 25 cm thick on all 78 beaches studied on the French coasts with an acoustic system [75]. There is therefore a difference along the beach cross-shore profile between the intertidal zone, which presents gas and water between sand grains, while the still-submerged part consists only of a mixture of more compact sand grain with water. The apparent density, which takes into account the density of fluids and solid particles, will therefore be completely different in these two adjacent environments. The presence of gas also affects resuspension because the water forms bonds with the sand grains that are weakened in the presence of gas. The bulk modulus of the fluid phase is strongly dependent on the gas content. For gas contents above 1%, the bulk modulus of the fluids is less than that of the sand matrix. For gas contents in the water–gas mixture between 0.1 and 1%, the compressibility of the liquid phase is of the same order as that of the sand matrix [92]. Density, porosity, grain shape, and gas content all have a direct effect on the settling velocity, which is a parameter modeled directly from grain size in models. Most studies of beaches take into account the relationship between sediment and slope. The global tendency is that the finer sand gives a very low slope (~1°), while the gravel can be piled up to 20°; between these two extremes sand beaches have a slope between 1 and 8° [73]. Wiegel’s (1964) relationship [93] between beach slope at the mid-tidal swell action zone and mean pebble size is based on measurements from several beaches on the west coast of the United States. He presents a relationship between beach slope and grain size, but also shows that this relationship varies depending on the exposure of the beach to waves. It should be noted that it separates the beaches close to the underwater canyon of La Jolla, indicating the possibility of local peculiarities. Bujan et al. (2019) [94], based on an exhaustive global bibliographic analysis, shows a relatively good relationship between gravel and slope, but for a given sand the slope can vary by a factor of 10. Analysis of the vertical variability of granularity shows that sediments are often stratified with a finer granularity of the first centimeter compared to the underlying sediments. This fact is constrained by the shape and density of these grains, hydrodynamic processes, and the granularity of the sediments available in the study area [20,82]. By mixing data from very different environments and probably sediments coming from different levels of the cross-shore profile, by omitting local specificities (bedding, grain shape, heterogeneity, density, etc.), the general studies necessarily give a poor correlation between slope and sand granularity. This is also due to the reduction of the sediment to a single physical quantity: the mean grain size. It is generally accepted that sediment size tends to decrease with distance from the shore, but the decrease is rarely monotonic, and enough cases have been found where sediment size increases offshore to suggest caution before invoking this empirical rule [95]. We thus have vertical variability over the first few decimeters and cross-shore variability of sediments that may respond to different and poorly defined rules between the inshore and offshore parts and from one environment to another. Establishing a relationship between slope and granularity based on different sets of measurements is therefore complex and marred by the mixture of data including different processes and different sediments with different properties erased by the average grain concept. An important parameter is sorting; McLean and Kirk (1968) [96] suggested that size is the primary control of sorting trends in the sediments studied, while hydraulic effects (wave action, etc.) contribute to variability or scatter of the data around the trend. Since size and sorting exert a primary influence on beach face slope through permeability, it is further suggested that, at least for the study beaches, trends in the size/slope relationship clearly reflect the characteristic local distributions of size and sorting. Sands have weak grain organization or no structure, poor water retention properties, high permeability, and high sensitivity to compaction. The geotechnical studies show that the presence of clays in sand, even in small proportions, can play an important role because they represent intergranular cement modifying resuspension properties (G7). However, very few articles discuss the influence of silt and clay on the morphodynamics of beaches. This is undoubtedly because the sedimentary dynamics of sandy beaches should normally be incompatible with the presence of fine particles in these environments. However, on all the sandy beaches studied on the French coasts, the average rate of silt and clay is 1.6%, and it can exceed 4% near rivers. The deposition of fine sediments favored by filter-feeding organisms and the reworking of these sediments by burrowing organisms that bring sediments to the surface do not respond to hydrodynamic forcing and must therefore be specifically managed in sectors where such sediments exist. Many coastal sedimentary processes and properties are influenced by the presence of offshore mud deposition; the ultimate stages observed on the coasts of Brazil [97] and India [98] are fluid mud deposits in the surf zone and the creation of mud banks after a storm, in which case the wave energy can be completely attenuated over a distance corresponding to a few wavelengths. Thus, sporadic mud deposition events during storms create short-term morphodynamic differentiation because sand and mud mixtures have different erosion rates, which vary as a function of mud fraction and mud dry density [99]. Beach sectors under the influence of mud in the sub-aerial beach, surf zone and part of the shoreline show low mobility and low hydrodynamics compared to sectors without mud [97].

3.7. Bedforms

Some classifications of beaches consider bedforms (G8). They are common and numerous but may be absent. They range from small ripples to ridges, berms, beach crests, swash marks, adhesion ripples, rill marks, scour-and-fill structures, and channel wave ripples/current ripples [100]. Beach ridges are a common feature of steep reflective and intermediate beaches, with dimensions ranging from a few centimeters to tens of meters. Their origin can be subharmonic standing edge waves or self-organization. Their dynamics originate from storms but can also be caused by the persistence of accretionary conditions that lead to a readjustment of the overall cusp spacing, implying the development of an alongshore continuous berm [101]. Unlike sedimentary fluxes, which are difficult to measure, large bedforms such as berms and bars have the advantage that their changes can be easily measured by topographic remote sensing (video cameras, drones, and satellites). It is also possible to detect and track underwater bars through the foam caused by the surge [102,103,104,105,106]. However, there may be a difference between the sediment fluxes and the dynamics of these structures because of the smallest bedforms, such as sand ripples and mega-ripples, to which is added the transport of sediment grains in suspension, which may be uncorrelated with the movements of large sandy bedforms. Within the family of large sediment bedforms, littoral sandbars have a special effect and need to be studied separately. Dean et al. (2013) [107] explain that they are one of the reasons for the difficulty in predicting decadal variability. They describe structures with a wavelength of 0.8 to 12 km, an amplitude of 40 m, and a width sufficient to cause positive or negative shoreline changes, moving along the shoreline at speeds of up to 1 km/yr.

3.8. Tectonic and Sediment Retention

Inland slope (G9): Due to the tectonic history, there is a correlation between the hinterland slope, the influx of sediments brought by the river system, and the width of the continental shelf, which determines its capacity to retain these sediments. This is summarized in the classification of Inman and Nordstrom (1971) [108]. For them, the first-order control of the morphology of a coastal zone comes from its tectonic history. They proposed that the most important factors in determining coastal characteristics are the location of the coast with respect to plate boundaries and the tectonic setting of the coastline. They distinguish collision coasts in subduction zones, which are tectonically active, with frequent earthquakes and volcanic activity. Thus, the hinterland includes mountains of more than 3000 m. The rivers that drain these coasts are relatively small and few, and the continental shelf is narrow with few depositional features. The opposite class in Inman and Nordstrom’s classification are the Marginal Sea Coasts, which occur along continental coasts with no tectonic activity. They represent broad continental shelves protected from open hydrodynamic ocean activity by other land masses, such as island arcs. The hinterland in this case is backed by hilly or low-lying regions with high sediment input from rivers, resulting in thick sediment layers and depositional sedimentary features. The dynamics of sandy beach morphology is particularly affected by the sinuosity of the coastline (G10). Purely rectilinear coasts develop in certain regions of low coastal relief with a large thickness of sediment, often homogeneous. This is the simplest system name, unconstrained open beach by Gallop et al. (2020) [109]. All other environments are geologically controlled beaches where physical boundaries such as headlands, outcrops, reefs, coastal platforms and islets determine beach boundaries, sediment supply, sediment type and morphological change. Geology can also interfere with the idealized cross-shore equilibrium envelope of the beach profile [109]. In the presence of beach crests, linear coasts still exhibit shoreline variability that affects sediment dynamics. Conversely, in the presence of rocky reliefs or defenses against erosion, such as groins or breakwaters, beaches may exhibit larger depressions. The curvature of the coastline can also be linked to the presence of islands or bays to create embayed beaches; in this case geological boundaries are a primary control on the morphodynamics [109]. Other cases of increasing shoreline sinuosity are the estuaries, deltas, and sandspit environments, which are directly related to the presence of rivers and the influx of sediments.

Earthquakes and associated uplift/downlift (G11) are also an important source of beach change along large regions of the world, such as in South America, around the Pacific Ring of Fire [110,111], and in the Indian Ocean [112]. The 2004 Indian Ocean tsunami devastated the west coast of the Andaman Islands (Thailand). The areas scoured by the tsunami showed a continuous recovery under the action of normal wind and wave processes or during the storm surges in the rainy season, completing the reversion to an equilibrium stage 2 years after the tsunami event [113]. In this environment, 60% of the sediment lost during the tsunami event was regained on the beaches within 6 months, and a post-tsunami progradation of 73 m/yr is observed [114]. This rapid beach recovery is due to the return of a large amount of sediment to reconstruct the beach, which are sedimentary structures adapted for tsunami protection [114]. Other studies conducted after the Sendai tsunami (Japan) provide the same evidence of relatively rapid beach recovery after severe erosion. Volcanoes also belong to the class of “Collision Coasts in Subduction Areas”, so the factors are similar and essentially geological, and the beach recovery is more frequently described. For them, Ramalho et al. (2013) [115] synthesize the factors that shape the coasts of oceanic island volcanoes, and they are: volcanism, tectonics, mechanical properties of shoreline lithologies, wave energy parameters, amplitude of eustatic change, mass wasting, subaerial erosion, sediment production and availability, reef growth and biogenic production, and uplift versus subsidence. When one or more of these factors change, feedback mechanisms immediately cause adjustments in the other factors, pushing the system back toward an equilibrium it never reached [115]. As an example of the first step of beach formation, on 19 September 2021, a new Strombolian monogenetic volcano erupted on the island of La Palma (Canary Islands, Spain); after the stabilization of the lava deltas, a beach-like formation appeared. These sedimentary structures were formed suddenly, less than 2 days after the stabilization of the lava fronts, and most were subsequently long-lasting. The analysis of the marine regime shows that these initial states of morphodynamic disequilibrium and adjustments seem to be progressing towards morphological stability and equilibrium [116].

3.9. Anthropogenic Effects on Beach Morphodynamics

Coastal regions are very complex environments with diverse hydrodynamic and biogeomorphic contexts and with important socio-economic and environmental problems. These systems are among the most affected by human impact through urbanization and port activities and industrial and tourism activities [28]. Human interventions that affect beach characteristics are numerous along the coastline, and they must be added to anthropogenic actions in watersheds of continental areas when they affect the sediment flows of rivers. The anthropogenic causes of beach erosion vary according to the natural physiography and the type of human activity. Listed in approximate order of dominance, the causes are: (1) disruption of natural longshore sediment transport patterns, (2) disruption of river-borne sediment delivery to the coast, and (3) subsidence due to groundwater withdrawal [107]. Human interventions can increase river loads (e.g., from deforestation and industrial agriculture) or decrease loads (e.g., by damming rivers), and there is often a sequence of these two human interventions [117]. Large dams are the primary source of sediment impoundment. Worldwide, large dams have trapped about 3200 Gt of sediment since 1950, of which about 74% would likely have reached the coastal ocean [117]. At the same time, there is a growing trend towards more natural antropogenic defense systems, where instead of hard structures, natural processes, if well understood, can be used to redistribute sediment and maintain a desired level of protection—e.g., the use of sand nourishment on the Dutch coast [118].

Ten pressure factors allow the synthesis of anthropogenic impacts: A1—Protection of coasts and rivers against erosion (seawalls, jetties, dikes, breakwaters, sand traps...); A2—Infrastructures of coasts and rivers (rigs, MRE, dams, bridges, and cables); A3—Sediment dumping.); A2—Coastal and river infrastructures (rigs, MRE, dams, bridges, and cables); A3—Sediment dumping; A4—Marine and river sediment extraction; A5—Beach nourishment; A6—Deforestation; A7—Trawling, recreational and commercial coastal fishing; A8—Dredging; A9—Beach cleaning and beach tourism activities; and A10—Gas or water extraction.

Coastal structures with the aim of fixing the coastline or combating the local effects of erosion have the objective of retaining sediments, so they generally imply the reduction in sediment fluxes in the downstream part of these infrastructures. Other important anthropic impacts come from the changes in the sediment supply of rivers due to human interventions, in particular with the dams and sediment extraction from river beds [15]. This dredging in rivers and harbors can sometimes be a kind of compensation in the marine environment with the dumping of dredged material, with additional potential impacts on bottom sediments and water turbidity [119]. Other human activities can have different levels of impact depending on their degree of activity, such as aggregate extraction [120,121], beach nourishment, fishing with towed gears, etc. They disrupt sediment transport and can reduce the available sediment supply in the coastal system [122]. In a less pronounced way, but with locally significant effects, pollution is the main cause of degradation of seagrass beds, mangroves and coral reefs. Such changes in the sedimentological environment can alter the roughness of the seabed or enrich beaches with clay particles. Each of these anthropogenic impacts is the subject of hundreds of articles that cannot be summarized here. For example, deforestation (A6) can have direct effects on coastal dynamics, and it causes accretion on the coasts of Madagascar because cyclones cause very strong erosion on the soil exposed by deforestation, and mountain slopes increase the erosivity of sediment flows. This results in widespread accretion of all the beaches of this Indian Ocean island [46]. In contrast, Addad and Martins-Neto (2000) [123] consider that erosion in the Alcobaca area (Brazil) is a direct consequence of deforestation in a place where wind–wave–littoral drift dynamics merely act as re-equilibrating agents. Another example is gas and water extraction (A10), which can cause land subsidence, which plays an important role in coastal retreat and increased flooding, as is the case on the coasts of Djakarta [124], Vietnam [125] and the Mississippi Delta [126]. In such environments, anthropogenic subsidence is the phenomenon that dominates coastal dynamics.

4. Review of Remote Sensing Sensors and Methods for Beach Monitoring and Prediction

Remote sensing has become indispensable for observing coastal ocean processes, but each platform has trade-offs [26,27,28], MODIS provides daily revisits at 1 km resolution, suitable for large-scale climatological studies but inadequate for resolving local shoreline changes. Conversely, Landsat 8/9 thermal sensors provide higher resolution (100 m) but at limited revisit intervals (8–16 days). Forthcoming missions such as Trishna (CNES/ISRO), LSTM (ESA), and SBG (NASA) promise daily revisits at ~50 m resolution, bridging current observational gaps. Despite recent advances [16,127], it is still considered problematic to extrapolate local process studies to global scales [128,129,130]. These debates on the intrinsics limitations of global studies also highlight the need for higher-resolution, regionally tuned assessments. Accurate numerical simulations of wave transformation, sediment transport, and morphodynamic feedbacks in the surf zone are still constrained by observation limitations and knowledge gaps, particularly in representing climatic influences on hydrographic and sedimentary forcing and boundary conditions such as regional settings (e.g., global waves ERA5 issues in the tropics, sea level at the coast from 25 km distant altimetry or CMEMS Global Ocean Physics Reanalysis (GLORYS)—both available on https://cds.climate.copernicus.eu, accessed on 15 January 2024) and topo-bathymetry.

Accurate topo-bathymetry is considered the cornerstone of beach monitoring and prediction. Forecast skill often degrades when models rely on outdated or incomplete bathymetric information. A range of observation strategies now exist, from direct in situ surveys to global satellite-based methods, each operating at different spatial and temporal scales and each subject to specific uncertainties (Table 1).

The most direct approach to mapping nearshore bathymetry remains acoustic soundings from research vessels and hydrographic surveys, including large-scale compilations such as GEBCO [131] and EMODnet [132]. More recently, crowd-sourced sonar from fishing fleets, recreational boats, and citizen science initiatives has emerged as a complementary source [133]. These data can substantially expand coverage, especially in heavily trafficked areas. However, vertical accuracy is highly variable, depending on instrument calibration, vessel motion, and user practices, while coverage in remote or low-traffic regions remains sparse. When carefully quality-controlled, these datasets can fill critical spatial and temporal gaps in bathymetric databases.

At the intermediate scale, similarly to shore-based cameras [134,135,136] or airborne lidar [137,138,139], unmanned aerial vehicles (UAVs) equipped with cameras or lidar provide centimeter-scale resolution of beach topography and very shallow bathymetry [140,141,142,143]. UAVs are flexible, cost-effective, and well suited for monitoring storm impacts or seasonal beach changes at site scale [24]. Their limitations are largely operational (weather, tides, and regulations) and environmental, as turbidity and breaking waves restrict depth retrieval to <2–5 m. UAV approaches therefore complement sonar surveys by resolving intertidal and shallow surf zones that are otherwise difficult to capture.

At larger scales, satellite-derived bathymetry provides global, repeatable coverage of shallow-water morphology [144]. Several techniques are available, each with distinct strengths and limitations [25]: Gravimetry has long been the primary method for deriving regional-to-global bathymetry, based on marine gravity anomalies [145,146]. Radar missions (e.g., Sentinel-1) enable wave-based bathymetry retrieval [147,148,149], a capability that will be extended with the forthcoming high-resolution SWOT mission (CNES/NASA) [150]. Lidar missions such as ICESat-2 show promising potential for shallow, clear-water bathymetry [151]. Optical radiometry, based on color-based depth inversion, remains the most established approach, using multispectral imagery (e.g., Landsat, Sentinel-2, WorldView, and Pleiades) [128,129]. Depth penetration typically reaches 10–20 m in clear waters but is strongly reduced in turbid surf zones. Errors range between 0.5–2.0 m RMSE depending on local conditions. Importantly, wave-based inversion techniques [130,131,132] can extend depth retrieval to 30–80 m, depending on the wave regime. Up to 1 min videos from space (e.g., Satellogic, JILIN, CO3D, and Planet missions) [152,153,154] have extended this potential. No single method is universally applicable, so fusion of multiple approaches, optical, radar, lidar, and gravimetry, represents the next generation of bathymetric datasets, with machine learning expected to play a critical role in harmonization and error reduction [155,156]. Analogously to global precipitation reconstruction—where regional-scale intelligent optimization is coupled with topography and an end-to-end network to enhance consistency [157]—multi-sensor fusion in coastal zones can likewise benefit from regional partitioning and topographic constraints to improve robustness and make error propagation explicit. From local to global scales, methods are often calibrated with in situ data for site-specific conditions [158], which limits comparability across environments. As a first step toward global implementation, preliminary methods have been developed using Sentinel-2 data [159], forming the foundation for recent large-scale efforts. The processing of these vast datasets is facilitated by high performance computing/datalake combined platforms such as Copernicus WEkEO or Google Earth Engine.

Finally, the integration of topo-bathymetric observations into coastal models through data assimilation frameworks [40,160] offers a pathway to dynamic, uncertainty-aware predictions. Sequential methods such as ensemble Kalman filters and variational approaches allow real-time updating of model states, reducing forecast drift and improving medium-term prediction reliability. This is important for ensuring that boundary conditions remain up to date, as Figure 4 highlights the strong influence of bathymetric state on beach predictions. The required frequency of observational updates depends on local variability timescales—ranging from storm-event triggers (e.g., after each major storm) in mid-latitudes to seasonal or multi-annual updates elsewhere [36]. However, assimilation remains computationally demanding and sensitive to assumptions about observation errors. Taken together (Table 1), these complementary approaches represent a continuum of topo-bathymetric observation strategies: from high-accuracy but spatially limited in situ surveys, through site-scale UAV monitoring, to regional/global satellite mapping, with data assimilation bridging observations and models. Their integration is essential for improving beach evolution forecasts. By explicitly accounting for the uncertainties of each method—ranging from turbidity and penetration limits to calibration errors—future monitoring systems can move toward dynamic, uncertainty-aware bathymetric updating, a prerequisite for robust and decision-relevant coastal prediction.

5. Discussion

5.1. The Case of the Langue de Barbarie Sandspit (Saint Louis, Senegal, West Africa): An Illustration of the Integration of Multi-Driver Processes and Remote Sensing Gaps

To be able to predict what a coastal environment will look like in the medium or long term, it is necessary to describe its characteristics as closely as possible, without having a risk-oriented vision (erosion and flooding), nor an objective of protecting benthic organisms, nor an objective of studying the effects of climate change: above all, it is necessary to study beaches without being guided in our choices by a limited list of input parameters such as those predefined by models or observations, or in other words without an a priori framework, Another question concerns the period of study or timescale considered, which will be important for the choice of such a pertinent observation and modeling framework.

The question of timescale is mostly focused on driving hydrodynamic processes, while phenomena such as sand storms, the release of dams or the development of algal fields, and many other phenomena, are not correlated with these hydrodynamic processes and do not have the same kind of periodicity. This focus on energetic hydrodynamic events such as storms is particularly related to the need to protect habitats from catastrophic events. This approach has its digital equivalent in very high-resolution models applied to constrained surfaces to account for effects such as the impact of sandbars on surging waves. Such infra-decametric resolutions require reducing the modeling area to a few square kilometers for a short period of time, which leads to removing all external factors from the modeled area and all long-term processes.

For the study of a beach over a period longer than a few weeks or months, the effects of the elements presented in the previous chapters will intervene and cause the beach to evolve, among which some will be essential, others will be secondary and others will be negligible. The first step in studying a beach should be to establish a list of local parameters using existing studies, field analysis and publicly available satellite data, such as Landsat or Sentinel series, before ordering more precise satellite images to answer specific questions. This analysis should make it possible to highlight the factors that could have an impact on local dynamics and their different temporal processes. Different analysis methods could then be implemented to complement the high-resolution models, such as machine learning of beach specificities from satellite data, or at least general sediment dynamic models to calculate sediment fluxes needed to provide input parameters to the high-resolution models.

But the previous studies show that the system is more complex than that, because discrete parameters such as the change in sediment density or an action leading to an increase in subsidence are not easily measurable. Therefore, in order to predict how a given beach will evolve, it is necessary to distinguish between factors that are important and those that are incidental to the given environment. Sediment transport in the coastal zone is the result of a combination of favorable processes that generate sufficient energy to mobilize and move the grains [161]. This is unfortunately continued by the movement of sediments described by size, density and shape moved by forcings that are swell, current and wind. But despite a drastic grouping of all the elements, at the end of this analysis we obtain 39 factors that can affect the sedimentary dynamics of a given sand beach (Figure 3). These factors add or subtract to promote or reduce sediment dynamics. Geological and climatic factors increase gradually over time, others such as wave action and biology have essentially seasonal effects, some such as tides have daily effects, and some are caused by exceptional one-off events (hurricanes, dam releases, beach nourishment, etc.). For a beach, a complete analysis must therefore prioritize all factors differently, depending on whether it is interested in short- or long-term predictions. For example, a climatic effect such as the increase in droughts (C3, C4, and C6) has a direct impact on river discharge, reducing both hydrodynamics (H1), water salinity (O6) and sediment inputs (S6). Latitude affects water temperature, marine organisms, and erosion rates, which in turn affect sediment availability. However, latitude is not estimated as a parameter because the specificities associated with latitude are integrated into the previous 39 factors. For example, erosion in the southwest of the Mekong Delta (Vietnam) is attributed to the cumulative effects of anthropogenic impacts, deficit of silt contribution caused by dams and deficit of sand from mining, added to a geological effect, local subsidence, and a meteorological process, the monsoon [15]. For a geologically constrained beach, such as the oceanic island volcanoes [115], we observe the following forcings: volcanism, tectonics (G9, G10, and G11), mechanical properties of shoreline lithologies (G4, G5, and G6), wave energy (O1 and O2), amplitude of eustatic change (C6), subaerial erosion (C4), sediment production and availability (G3 and G6), biogenic production (B3 and B5), and uplift/subsidence (G1). Island volcanoes are probably the extreme case with the greatest number of geological factors. They thus define the extreme where geology is the dominant factor in beach dynamics. But volcanoes are also environments where anthropogenic actions and biology can play an important role, as seen on the island of Mauritius, where the factors contributing to the coastline changes of the island of Mauritius are attributed to eight natural phenomena, tectonic, eustatic, isostatic rebound, volcanic, biological, sedimentary, climatic and thermal, and a variety of anthropogenic factors: sand mining, salt industry, fish farming, port infrastructure, dune planting, pass opening, agriculture, tourism, aquifer, coastal protection [162]. Thus, for a simple isolated volcanic island in the Indian Ocean, such a prioritization of impacts cannot be generalized but must be defined by cells with similar hydrodynamics and characteristics to ensure that all these impacts are taken into account.

If we look at a beach less than 10 km from the mouth of the Senegal River, we observe the anthropogenic impact of the protective dike of the city of Saint-Louis (A1), the release of dams (A2), the recharging of the Gandiol beach (A5), a significant sedimentary flux (G3) of weakly bedded medium sands (G4 and G5) with shoreline undulations (G10), waves (O1), sandbars and the passage of sand waves (G8), the seasonal variation in the river flow (H1), which generates an average dynamic of 1.4 m/year of the sandspit called Langue de Barbarie [1,20]. To these factors must be added some more difficult to quantify impacts such as coastal upwelling currents (O4), sandstorms (C4), sediment retention by filaos plantations (B5) [55], the weakening of sand layers by crab burrows (B4) and beach cleaning (A9). In such an environment, if the remarkable facts of marine submergence point to storm waves (O2) as being responsible as the cause of flooding, it turns out that fifteen other factors generate a complex sedimentary dynamic that generates the resilience of the sandspit, but also a dynamic that the new satellite data show to be very irregular in time on the scale of days, months and years, and in space [20].

Following an analysis of the factors influencing the dynamics of the Langue de Barbarie, Senegal, West Africa, supplemented by data from various other sites, an empirical classification of the elements affecting the evolution of the morphology of reflective beaches was established (Figure 4). This analysis identified 21 factors that are important for improving forecasts of the future of coastal areas, including sediment availability and human impacts. The Eisenhower matrix for the Langue de Barbarie, Senegal, categorizes various factors influencing coastal dynamics according to their importance and ease of implementation. The upper left quadrant highlights important but easily manageable factors such as shoreline slope and sediment granularity, making them high priority for intervention. In contrast, the top-right quadrant contains important but difficult to address factors such as initial bathymetry, anthropogenic impacts, and sandbar variability, which require more complex or long-term strategies. The lower left quadrant contains less important, easily managed factors such as riverine inputs, while the lower right quadrant contains less important and challenging aspects such as aeolian sand inputs and infra-gravity waves, which are of low priority. This matrix helps streamline decision making by focusing on the most effective and feasible interventions.

5.2. The Need for a Holistic Complex Approaches in Observing and Predicting Beach States: From Simplification to Complexification

The evolution of coastlines is due to meteorology, oceanography, geology, and the action of organisms and humans. Wave physics has been the subject of major research over the last few decades, leading to precise characterisation of wave hydrodynamics. But even in this field, for the results to be satisfactory, an initial terrain model with a sub-decametric mesh that is up to date with the latest developments is required. High-performance modelling is limited to a few locations where recurrent surveys have been carried out for many years (e.g., Duck, USA). Over shorter periods, measurements are also taken in anticipation of the construction of coastal infrastructures. But for the rest of the world’s coasts, bathymetry is obsolete and the other forcing factors that affect coastal dynamics are poorly known. As a result, apart from a few research sectors or industrial studies, the models are only reliable for a few tens of hours and then deteriorate under the effect of erroneous changes in morphology. Relying solely on in situ measurements is challenging due to the difficulty of achieving the necessary recurrence to capture all coastal processes, and the location of these measurements significantly impacts the results. The observation and prediction of beach states present novel challenges and insights, particularly in the context of evolving classification systems and the increasing availability of sophisticated tools such as numerical models and satellite observations [127,144].

Historically, beach classifications have been categorized into three main types: naturalistic approaches aimed at mirroring reality, geomorphological approaches that categorize environments by key physical characteristics, and classifications based on underlying physical processes. However, no existing classification system has managed to encompass the full complexity of beach environments, which consist of numerous factors ranging from sediment availability to hydrodynamic conditions.

Pioneering work [30] offered one of the first comprehensive beach classifications, considering a wide range of variables such as sediment types, latitude, climate, and energy levels, yielding up to 72 distinct coastal environments. Later modifications by Fairbridge (2009) [163] introduced anthropic and biological factors, acknowledging human influence on coastal dynamics. Shepard (1937) [29] and subsequent studies shifted the focus to the agents—marine or terrestrial—shaping coastal landscapes. More recent work [34,164] emphasizes hydrodynamic processes and morphological inputs like beach profiles and wave climates.

However, no existing classification fully integrates all 39 relevant factors, underscoring the need for an analytical approach that prioritizes specific variables based on the unique characteristics of each beach. Given the variability in beach environments, any attempt at a universal classification is impractical. Instead, beaches should be characterized based on their distinct local conditions, which may fluctuate significantly due to seasonal changes, geological events, or anthropogenic influences. This variability highlights the need for more tailored and dynamic approaches to beach classification, moving beyond simple parameter-based groupings.

As beach research increasingly relies on numerical models, there has been a trend towards simplifying these classifications. Yet, with the growing use of high-resolution satellite data and the increasing number of observational datasets, it is becoming clear that this simplification may overlook critical complexities of coastal systems. This shift emphasizes the need to capture the full range of factors, including non-hydrodynamic phenomena, within models. Machine learning and statistical models offer promising avenues to incorporate these complexities and enhance predictive capabilities.

Table 2 presents 39 factors influencing the morphodynamics of sandy beaches, categorized into five domains, Anthropogenic (A), Biological (B), Climatological (C), Geological (G), and Hydrographic (H), along with Oceanographic (O) factors, and say if these factors are captured by in situ measurements, remote sensing, and modeling. This framework emphasizes the complexity of beach systems, recognizing a wide range of physical, biological, and human-induced variables that affect coastal dynamics. Notably, different observation and prediction methods are linked to each factor. For example, wave characteristics (O1), infra-gravity waves (O3), and sediment granularity (G4) are measurable by in situ methods and remote sensing, while climatic phenomena (C3) and sediment availability (G3) are primarily modeled. Table 2 underscores the importance of combining multiple observational tools—field data, satellite imagery, and numerical models—to capture the complexity of coastal processes. This approach is essential for improving predictive models and understanding long-term coastal evolution, as no single method can fully encompass all the factors affecting beach morphodynamics.

In particular, advances in satellite technology, especially when coupled with machine learning [156,165], could revolutionize beach classification and prediction. High-resolution, high-frequency data from satellites can provide unprecedented insights into coastal dynamics, particularly regarding anthropogenic impacts and natural variability. These tools offer the potential to refine in situ measurements and improve the accuracy and relevance of observational data.

Moreover, as numerical models are critical for predicting long-term coastal evolution, there is an urgent need to improve their accuracy and reliability. While some skepticism remains about the feasibility of predicting shoreline changes over multidecadal scales [166,167], combining quantitative models with expert judgment and qualitative approaches offers a path forward. The inherent variability in natural beach systems means that statistical approaches will remain essential for making predictions over longer timescales [168]. Prediction studies also face challenges in evaluating different numerical models due to their complexity and the absence of standard metrics for comparison.

The integration of field measurements, remote sensing technologies, and numerical simulations, such as using data assimilation systems, will be essential to refine predictive models and enhance our understanding of coastal behavior (Table 3). In conclusion, the way forward lies not in oversimplifying beach classifications but in developing tools that allow for the nuanced characterization of each beach based on its specific attributes, making use of advanced satellite data and machine learning to improve future coastal predictions.

5.3. Perspective of Satellite Earth Observation for Dynamic Coastal Digital Twins

Satellite Earth Observation has advanced rapidly in recent years, offering new opportunities to monitor beach evolution and coastal hazards at unprecedented scales. Missions such as Sentinel-1/2/3, SWOT, and CO3D now provide global coverage of sea level, waves, shoreline change, and coastal morphology [169,170,171]. However, despite these advances, most EO-based coastal applications remain localized or regional in scope, and their integration into planetary-scale, dynamic beach evolution forecasting is not yet operational.

There are three major limitations that currently hinder the use of Earth observation for coastal digital twins. The first is the temporal scale lock: while satellites provide daily or weekly overpasses, most products (e.g., CoastalDEM and ALOS World 3D—30 m) are static or updated infrequently, often relying on monthly or annual composites. Such approaches cannot capture the rapid morphological adjustments of dynamic beaches. To overcome this, monthly or even weekly dynamic coastal elevation updates can be derived by fusing multi-mission radar and optical data. The second limitation is the land–sea interface lock, where the nearshore zone (0–30 m depth) remains poorly resolved. This zone controls wave transformation, sediment transport, and flood propagation yet is a persistent blind spot in global datasets. A combination of various complementary methods, wave-based inversion (e.g., SWOT and Sentinel-2), optical radiometry (e.g., multispectral color-depth inversion) [172,173,174,175,176], and intertidal mapping [177] could be used to produce seamless, high-resolution topo-bathymetry. The third limitation is the fragmentation of methods and missions. Radar, optical, and lidar techniques are often applied independently, developed for local studies, and lack interoperability. This can be addressed by establishing a unified processing chain, harmonizing multi-mission datasets (Sentinel series, SWOT, CO3D, ICESat-2, and Landsat) with machine learning [155,156] to consistently derive global coastal topography and bathymetry [156]. Complementary methods, such as multi-sensor waterline tracking (Sentinel-1 and -2, e.g., [178]), stereo-photogrammetry (CO3D and Pleiades [22]), and radar-based slope estimation, will further enhance accuracy.

A future outcome could be a dynamic, satellite-derived coastal digital elevation model capturing the full topo-bathymetric continuum and its variability. This dynamic observation can directly feed into hybrid forecasting models that integrate physics, climate variability, and AI. Together, these developments would mark a step-change toward coastal digital twin capable of supporting probabilistic, scenario-based early warning systems. In this way, Earth observation will transition from descriptive monitoring to assimilation and forecasting.

5.4. Modeling Hybdrid Framework and Uncertainty in Beach Evolution Prediction

Accurately forecasting coastal hazards from storm events from the local to planetary scale requires a hybrid and modular multi-scale modeling framework that balances physical realism, computational efficiency, and global scalability. Coastal systems are inherently nonlinear and multi-driver—where tides, waves, surges, river runoff, and evolving morphology interact in ways that challenge single-model approaches. Other influences such as biology and socio-ecosystems also have the potential to largely influence this evolution. This gap can be addressed by blending three complementary model classes: (1) conceptual models (e.g., coastal-XRO, [179]) that directly link large-scale climate oscillations such as the El Nino Southern oscillation and North Atlantic Oscillation to coastal impacts with simplified, computationally efficient dynamics; (2) process-based models (e.g., ShorelineS, [180]; COSMO-COASTS, [181]; LX-Shore, [182]; and GLOBCOAST; [183,184]) that explicitly simulate hydro-morphodynamical interactions for physically grounded forecasts; and (3) AI-enhanced models using symbolic regression, large language models, and machine learning emulators [185,186,187] to both improve parameterizations and provide fast, data-driven surrogates. This modular architecture might enable forecasts accounting for the diversity of coasts worldwide, integrating underrepresented drivers such as nonlinear processes and interdisciplinary factors [188]. By training and benchmarking these models with global Earth observation datasets and linking them to climate modes offering windows of predictability [189,190] and forecasts (e.g., ECMWF and CMEMS), advances can be achieved toward probabilistic, ensemble-based prediction of coastal hazards anywhere, anytime. This multi-scale, multi-driver integration directly addresses data and scenario uncertainty by embedding observations dynamically into model states, while maintaining physical interpretability.

The prediction of sandy beach evolution is fundamentally constrained by multiple sources of uncertainty that propagate throughout the observation–modeling–forecasting chain (Figure 5). Addressing these uncertainties is essential to improve forecast skill and to provide decision-makers with probabilistic rather than purely deterministic outcomes.

Data uncertainty arises from limitations in measurements and observations. For remote sensing systems, this includes sensor resolution, revisit frequency, georeferencing accuracy, cloud cover in optical imagery, water turbidity, and calibration errors [12,19,25]. Even when high-resolution data are available, gaps in temporal coverage may introduce aliasing effects, particularly in highly dynamic coastal environments.

Parameter uncertainty stems from the imperfect quantification of key processes in morphodynamical models. For example, sediment transport coefficients, bed roughness parameters, and vegetation drag terms are typically calibrated from local datasets and may not be transferable across sites or time scales. Small errors in these coefficients can compound, resulting in significant divergence of long-term predictions [191,192,193]. Structural uncertainty reflects the limitations of model formulations themselves. While short-term wave and current dynamics are increasingly well represented, many models still omit important processes, such as biological interactions, human interventions, or complex feedbacks between hydrodynamics and sediment supply [3,194]. Simplifications, such as using two-dimensional rather than fully three-dimensional approaches, may further limit predictive accuracy. Scenario uncertainty arises from the variability in future external drivers, both natural and anthropogenic. Climate change introduces uncertainty through sea-level rise trajectories, storminess regimes, and shifts in large-scale climate oscillations [195]. Human activities, including coastal defense construction, dredging, nourishment, and river regulation, introduce additional unpredictability that is difficult to capture in deterministic simulations [14,15].

These uncertainties inevitably combine and propagate into predictive outputs (Figure 5), shaping the confidence with which we can anticipate shoreline change, beach volume loss, or flooding risk. Rather than concealing this complexity, modern approaches such as Monte Carlo simulations [180,183] and Bayesian error propagation [196,197] explicitly quantify how data, parameter, structural, and scenario uncertainties influence forecasts. Such methods enable the shift from single deterministic projections to probabilistic prediction envelopes that transparently communicate confidence intervals and plausible ranges of outcomes. This transition is critical for actionable coastal risk management and climate adaptation. Beyond technical advances, embedding uncertainty into narrative-driven scenarios can transform how forecasts are used by society [198]. Instead of presenting one “most likely” future, models can generate a suite of what-if stories, grounded in simulation, that link physical uncertainties with possible human choices. For example, a coastal town could explore contrasting futures: one where a seawall is built, another where wetlands are restored, and another where no intervention is taken. Each storyline tests how these strategies interact with uncertain drivers like sea-level rise, storm intensification, or sediment supply changes. This scenario-based storytelling provides a safe rehearsal space where communities and decision-makers can evaluate trade-offs, stress-test adaptation plans, and refine early-warning strategies before crises occur. In practice, the way forward is to report, for key outputs such as shoreline position, confidence intervals (e.g., 90% or 95%) derived from Monte Carlo sampling of the main input distributions (with sample sizes typically N ≥ 1000–10,000) or from simplified Bayesian error propagation schemes that yield posterior intervals—thereby making uncertainty quantification transparent, reproducible, and decision-relevant [192,199].

By uniting uncertainty quantification with scenario generation and human action pathways, coastal forecasting frameworks can evolve from static predictions into dynamic decision-support systems. Such systems not only deliver probabilistic, scientifically robust forecasts but also empower local stakeholders to imagine, prepare for, and adapt to a range of possible futures—making coastal management more resilient in the face of uncertainty.

6. Conclusions

Predicting the future evolution of sandy beaches remains a significant scientific and societal challenge, primarily due to the multitude of interacting factors and the limitations of existing observations and models. Although short-term, wave-dominated dynamics are now well understood, long-term prediction is still affected by model drift, outdated bathymetric data and the omission of geological, biological and anthropogenic processes.

This review emphasizes the pivotal role of remote sensing, and particularly Earth observation satellites, in enhancing the capacity to monitor the coastline across different scales, offering consistent observations of shoreline changes, sediment dynamics and environmental forcing factors. However, significant uncertainties remain at every stage of the forecasting process, from data acquisition to model structure and scenario assumptions. Addressing these uncertainties requires the adoption of rigorous frameworks for uncertainty quantification, including the explicit characterization of data, parameter, structural and scenario uncertainty and how these propagate into predictive outputs.

Future research should focus on three main areas. Firstly, it should focus on the development of operational strategies for the dynamic updating of nearshore bathymetry, leveraging satellite-derived bathymetry, UAV surveys and the assimilation of data into models. Secondly, it should focus on advancing multi-sensor integration and remote sensing–model fusion, combining observations from optical, radar, lidar and video systems with process-based and data-driven models. Thirdly, probabilistic, multi-driver forecasts should be developed that capture both physical and human influences. These forecasts would provide decision-makers with more reliable predictive envelopes rather than deterministic outcomes.

Embracing these approaches will enable the coastal science community to progress beyond wave-only paradigms and develop genuinely integrated, uncertainty-aware and decision-relevant coastal forecasting systems.

Author Contributions

T.G. and R.A. conceptualized and wrote the article. E.W.J.B. participated in discussions and writing of the manuscript. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1 Factors influencing the sedimentary dynamics of sandy beaches at a regional scale (hundreds of kilometers) and a local scale (some kilometers).

Figure 2 Conceptual framework of interactions between physical and biological parameters influencing beach dynamics (adapted from [31]).

Figure 3 Summary of the 39 factors listed in this study influencing the morphodynamic of sandy beaches. A: Anthropogenic, B: Biological, C: Climatological, G: Geology, H: Hydrography, O: Oceanography.

Figure 4 Eisenhower matrix applied to the Langue de Barbarie, Saint Louis Region, Senegal (West Africa). Aerial view of Saint Louis (credits. R. Almar—IRD/LEGOS).

Figure 5 Uncertainty in beach evolution prediction. Uncertainty arises from data, parameter, structural, and scenario sources, all of which propagate through models into predictive forecasts. Monte Carlo or Bayesian approaches can be used to quantify and visualize these effects as probabilistic confidence intervals.

Table 1

Remote sensing sensors and their relevance for beach evolution studies.

Sensor/Platform	Main Drivers/Processes Captured	Spatial Scale	Temporal Scale	Uncertainties/Limitations
Optical satellites (Landsat, Sentinel-2, PlanetScope)	Shoreline position, beach width, sandbar migration, vegetation cover	10–30 m (Planet: ~3 m)	Days–weeks (5–16 days)	Cloud cover, tidal aliasing, water turbidity, georeferencing errors
Radar satellites (Sentinel-1, TerraSAR-X, RADARSAT)	Wave fields, runup extent, inundation, moisture content, storm impact mapping	10–100 m	Days–weeks (6–12 days typical)	Speckle noise, coastal layover/shadow, requires advanced processing
Altimetry (Jason, CryoSat-2, Sentinel-3, SWOT)	Regional sea level, wave height, storm surges	km-scale footprints	10–35 days repeat cycles	Low nearshore resolution, coastal retracking errors
Satellite-derived bathymetry (Sentinel-2 MSI, Landsat-8/9 OLI, ICESat-2, Pleiades, WorldView)	Nearshore topo-bathymetry, sandbar dynamics	1–30 m (sub-meter for commercial)	Days–months	Depth penetration limited (<20 m, turbidity dependent), atmospheric correction uncertainties
UAV photogrammetry/lidar	High-resolution topography, dune/beach profiles, vegetation, nearshore bathymetry (clear waters)	cm–dm	On-demand (minutes–days)	Weather/wind limits, processing effort, small-area coverage
Coastal video monitoring (e.g., ARGUS, fixed cameras)	Shoreline position, swash/runup, bar morphology, wave period	0.5–5 m	Seconds–minutes (continuous)	Restricted to instrumented sites, calibration drift, limited coverage
Airborne lidar (topo- & bathymetric)	High-accuracy elevation, dune/beach morphology, shallow bathymetry	0.5–2 m	Campaign-based (months–years)	Costly, limited repeatability, water penetration affected by turbidity

Table 2

39 identified factors in Section 3 influencing the morphodynamic of sandy beaches and the study approaches. (A: Anthropogenic—not in Table), B: Biological, C: Climatological, G: Geology, H: Hydrography, O: Oceanography. Blue triangle, yellow rectangle and green pentagone indicates its capture by tools/approaches, in situ measurement, remote sensing and modeling, respectively (see Table 2). A tick box means that the factor can be addressed using this tool or approach.

Code	Factor	In Situ Measurement	Remote Sensing	Modeling
O1	Waves	✅	✅	✅
O2	Waves characteristics during storm	✅	✅	✅
O3	Infra-gravity waves	✅	✅	✅
O4	Water level	✅	✅	✅
O5	Water level during surges	✅	✅	✅
O6	Sea water P°, T°, Salinity	✅	✅	✅
O7	Existence and location of the breaking of the waves	✅	✅	✅
O8	Up-welling currents	✅	✅	✅
H1	Modification of currents and of salinity by river fluxes	✅	✅	✅
H2	Level of the water table	✅		✅
C1	Weather	✅	✅	✅
C2	Impact of melting ice and sea level rise		✅	✅
C3	Consideration of climatic phenomena (monsoon, hurricanes, melting ice)		✅	✅
C4	Aeolian transport, particularly sand storms	✅	✅	✅
C5	Impact of ENSO		✅	✅
C6	Evolution of these elements with the climate change including the sea level rise		✅	✅
B1	Algae, Seaweed	✅	✅
B2	Biodeposition, Posidonia wrack, …	✅	✅
B3	Reefs and bioherms (corals, Sabellaria, …)	✅	✅
B4	Benthic colonies (oysters, Crepidula, cockles, mussels, Lanice conchilega, …)	✅	✅
B5	Beach aerial plants (Crambe maritima, Abronia maritima, Atriplex leucophylla, Filao …)	✅	✅
G1	Subsidence and postglacial rebound	✅		✅
G2	Beachrock	✅	✅
G3	Sediment availability (thickness and external contributions)	✅	✅	✅
G4	Granularity of sediments	✅	✅	✅
G5	Shape (shells/rounded grains) of sediment grains	✅	✅	✅
G6	Porosity, gas content and fall velocity	✅	✅	✅
G7	Clay content	✅	✅	✅
G8	Bedforms	✅	✅	✅
G9	Hinterland slope	✅	✅	✅
G10	Sinuosity of the shoreline	✅	✅	✅

Table 3

Factors influencing sandy beach dynamics and their observation/modeling approaches as documented in Table 2.

Factor Domain	Representative Factors	In Situ Measurements	Remote Sensing	Modeling
Oceanographic	Waves, tides, storm surges, infragravity waves, currents, upwelling	Buoys, ADCPs, tide gauges	SAR (waves), altimetry, optical shoreline proxies, video monitoring	Hydrodynamic & wave models (WW3, XBeach, CROCO)
Climatic	ENSO, monsoon, storms, sea-level rise	Weather stations, river gauges	MODIS/VIIRS (SST), Sentinel-3, GRACE (water mass changes)	Climate & Earth system models
Geological	Sediment supply/thickness, grain size, bedrock, tectonics, subsidence	Cores, seismic surveys, sediment sampling	UAV/satellite-derived bathymetry (grain size proxies), InSAR (subsidence), lidar	Morphodynamic models with sediment transport modules
Biological	Reefs, seagrass, wrack, dune vegetation, bioturbation	Ecological surveys, biomass sampling	Optical/UAV (vegetation indices, reef mapping)	Eco-morphodynamic coupled models
Anthropogenic	Dams, dredging, nourishment, hard defenses, urbanization	Field surveys, sediment flux monitoring	UAV/optical (land cover, shoreline change), AIS (ship traffic), nightlight data	Socio-economic & engineering impact models
Hydrographic	River discharge, salinity, groundwater inputs	River gauges, water sampling	MODIS/Sentinel-2 (turbidity, salinity proxies)	Watershed & sediment delivery models

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