Application of Remote Sensing Technology in Monitoring Sea Level Rise Due To Climate Change: A Systematic Review

Remote sensing technology for environmental monitoring

Remote sensing technology has enabled breakthroughs in understanding dynamic processes on the planet, such as those in the oceans and atmosphere. Remote sensing is an observation technique that uses sensors installed on board satellites and aircraft to gather information about the Earth’s surface without direct physical contact. This technique promotes the study of Earth’s surface over vast spatial domains and temporal resolutions, thereby allowing the observation of natural processes over the entire lifetime of Earth (Cazenave & Moreira, 2022). In marine research, remote sensing uses various tools, including radar altimeters, satellite gravimeters, optical altimeters, and laser altimeters. Radar altimeters launched during the TOPEX/Poseidon mission, which began in 1993, were designed to detect tiny changes on the sea surface and provide an accuracy of up to 4 cm per day (Ballarotta et al., 2019). On the other hand, satellite gravimetry, such as the Gravity Recovery and Climate Experiment (GRACE) mission, provides information on the distribution of seawater masses resulting from climate change.

The primary advantage of remote sensing lies in its ability to produce global, long-term data that provide inherent and contextual information for sea-level observations. This technique makes it easier to gather information about areas that are either difficult to access or dangerous, serving as the cornerstone of informed decision-making. In addition, remote sensing is the preferred strategy for environmental observation, especially regarding the examination of land use and land cover variations (Jensen, 2014) and the observation of the impact of climate change, primarily based on the measurements of sea-level increase and the distribution of various greenhouse gases (Masria A., 2024). Some studies have used not only one type of data but also combined satellite altitude measurements and LiDAR data to chart coastal elevation and assess the risk of sea-level rise (Bogning et al., 2018; Chust et al., 2008; Rizzo et al., 2025). Altimetry data have enabled sea-level observations for three decades, and the use of optical and LiDAR provides information for coastal observations. Therefore, this technique serves not only as an instrument for scientific study but also as the cornerstone for making environmental policy and adapting to climate change, as shown using the European Union’s Copernicus program (Melet et al., 2021).

The role of remote sensing in identifying sea level rise

The application of remote sensing technology to sea level rise has been considered a breakthrough in climate research. Satellite altimetry enabled the measurement of sea surface levels on a global and daily basis from 1993, hence delivering very accurate and consistent long-term records. Long-term trends and acceleration in sea level rise can be detected using this data, while natural climate variability and changes resulting from anthropogenic activities are distinguishable (Nerem et al., 2018). A number of remote sensing technologies have an important role in understanding sea level rise and coastal vulnerability. Detecting changes in seawater mass due to melting of land ice and variation in land water storage is done by GRACE gravimetry data, while changes in coastal land deformation are observed by SAR. Altimetry and LiDAR support flood modelling and coastal spatial planning with high-precision elevation data, resulting in more accurate projections of seawater intrusion and land loss (Cazenave & Le Cozannet, 2013). Synergy between technologies, including ICES at laser altimetry and tidal data validation, reduces uncertainty and produces more reliable information.

Studies on sea level rise using remote sensing are generally still partial, both in terms of region and method, making them difficult to compare due to differences in sensor usage, such as altimetry, gravimetry, and LiDAR. Several remote sensing-based studies highlight only technical aspects or global trends, while systematic studies on the effectiveness and accuracy of technology remain limited, as explained in Legeais et al., (2021). The other studies show that sea level rise studies focus more on identifying global trends than evaluating the accuracy of methods, as demonstrated by Dash et al. (2025)‘s analysis of sea level anomalies in ocean parameters, and Hamlington et al. (2023) on the acceleration of sea level rise in the last three decades.

The study by Jia et al. (2022) compiled multi-satellite altimetry data to calculate sea level changes technically without discussing field validation in depth. The accuracy and robustness of Global Mean Sea Level (GMSL) observations are a considerable methodological concern that needs to be overcome (Guérou et al., 2023). However, the search for the most reliable technologies for continuous observation and validation of climate modelling requires an extensive survey of the literature to assess research trends, capabilities, and limitations. In an attempt to merge and evaluate advancements, the study appears to focus on the use of the Systematic Literature Review (SLR) method for two primary purposes. Firstly, there is interest in exploring the use of remote sensing technology to detect and monitor sea level rise. In essence, the study seeks to assess which of the available technologies for sea level increase observation are most pertinent and reliable for the observation process.

This leads to the development of two major research inquiries that guide the study and are addressed to enhance research and support the development of correlated adaptive and mitigation measures for sea levels. These research inquiries are as follows: (1) What remote sensing method can be most applicable for sea-level rise observation? and (2) How do the accuracy levels of the various observation methods differ for comparison consideration?

Research design

Research on the use of remote sensing satellites to monitor sea level rise due to climate change was reviewed by combining various literature from geography and environmental science, thereby providing an accurate understanding of the role of remote sensing technology in monitoring and evaluating environmental conditions. This method was carried out systematically with reference to the PRISMA protocol (Moher et al., 2009), as well as methodological updates refined in PRISMA 2020 (Page et al., 2021).

Data-based and search strategy

To ensure a comprehensive and high-quality literature synthesis, this study utilized two prominent scholarly databases: Scopus and Web of Science (WoS). Scopus was selected for its extensive coverage of social and environmental sciences (Shaffril et al., 2018), while WoS provided access to highly reputable, peer-reviewed journals across multidisciplinary fields. The search was conducted in August 2025, focusing on the most recent decade (2016–2025) to capture advancements in satellite-based monitoring and the latest generation of remote sensing technologies. The following search string was employed to identify relevant literature:

The Boolean operator “OR” was applied to “Sea Level Rise” and “Shoreline Changes” to broaden the scope, ensuring the inclusion of studies discussing either phenomenon or their interrelationship. Initial searches yielded 821 records from Scopus and 945 records from WoS.

Inclusion and exclusion criteria

To maintain academic rigor, the retrieved documents were screened based on predefined inclusion and exclusion criteria, as detailed in Table 1. Only peer-reviewed journal articles published in English and available via Open Access were retained. Following the initial screening, the pool was refined to 233 articles from Scopus and 395 from WoS, resulting in a final total of 628 articles for eligibility assessment.

Screening criteria

Before screening, to avoid duplication of similar articles in one database with another, articles suspected of being duplicates were resolved using the Rayyan online platform. The results revealed that 175 articles in the Scopus database had a similarity of over 95% to articles in the WoS database, resulting in a total of 453 articles entering the screening stage. The first screening stage examined the title and abstract of each article. Articles irrelevant to the research topic and objectives were then ‘excluded’. Articles that clearly had a scope in accordance with the research questions that had been formulated were ‘included’. In contrast, information lacking detail in the title and abstract was included in the ‘maybe’ option for further full-text screening. This process was carried out collaboratively as a team to maintain consistency and validity, resulting in more accurate and accountable decisions. A total of 369 articles were excluded, consisting of 143 articles with wrong outcomes or research results that did not match the focus of the study, 130 articles with study designs that were not relevant to the required research design, and 96 other articles that were not included because their topics were not directly related to the main issues discussed in this study, leaving 84 articles included for full-text screening.

The second screening stage involved selecting articles by reading the full contents of each selected article in detail. The authors divided the tasks for this stage, agreeing on specific provisions that specifically address the use of remote sensing in monitoring sea level change. Of the 84 articles, 15 met the eligibility criteria based on the research purposes ( Figure 1).

Figure 1. PRISMA diagram showing the systematic literature review procedures.

Quality appraisal

Quality assessment was conducted on 15 articles that had passed the screening stage based on the PRISMA protocol. This process involved systematic evaluation using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for cohort studies. Although this tool is widely used in medical and social research, several types of checklist tools, including one for cohort studies, are relevant for quantitative, observational, and temporal research, with some adjustments to the questions. This step evaluates the risk of bias in the primary studies by asking questions about each sample article/study related to exposure assessment, measurement of exposure and outcome variables, control of confounding factors, and appropriate statistical analysis. Answers are determined by selecting one option (yes, no, unclear, or not applicable). Based on the cumulative score for each question, all article samples can be categorized as meeting the quality standards for inclusion in the research.

Data extraction

During the data extraction stage, document characteristics, including study information, study area, study design, and research limitations, were identified. This data was used to classify the types of remote sensing widely used globally to monitor sea level rise, based on a sample of articles that had passed the eligibility criteria. Details of the data extracted from the selected studies are presented in Table 2.

Evolution of publication

The number of publications, prominent journals, and authors in sea level rise research has increased since 2017. Early research in 2017 focused on regional sea level rise. The use of advanced methods, such as GNSS reflectometry, virtual tide gauge data, and analyses of vertical land motion, represented a major expansion during the 2018–2019 period. Research on sea level rise was improved in 2020 by increasing the application of remote sensing methods. This field peaked in 2021 with the highest number of research articles covering a wide range of topics, including regional sea level dynamics, decadal climate variability, and contributions from Copernicus satellites. The research remained consistent from 2023 to 2024, with a focus on satellite-based monitoring to support adaptation policy and long-term trend assessments in the North Atlantic. The most recent study, which was released in 2025, underscores the importance of regional dynamics by discovering that shifts in water balance have reduced sea level rise in the Eastern Mediterranean. Research from 2017 to 2025 shows an increasing trend overall, with 2021 marking the peak of methodological innovation and scientific output in SLR impact studies. Key information regarding the included studies is summarized in Table 3.

Global distribution

Research on sea level rise highlights the importance of robust cross-border collaboration, with studies tailored to the unique geographical and environmental challenges of each region. China is a leading contributor, conducting research on the spatial variability of relative sea level rise in Tianjin and spearheading the large-scale Dragon IV project, which applies Earth Observation technologies to monitor coastal zones in China and the Saint Petersburg region of Russia (Tang et al., 2021; Wang et al., 2020). The United States has also played a central role through long-term investigations along the Northwest Atlantic and Hawaiian coasts, as well as joint research with Mexico in the Gulf of Mexico that integrates GNSS, tide gauges, satellite altimetry, and geodetic models to assess interactions between sea level rise and vertical land movement (Wang et al., 2020). In Europe, Portugal has a well-developed understanding of seasonal, interannual, and multidecadal sea level variability along the Iberian coast and North Atlantic islands (Biguino et al., 2024). In contrast, Estonia has focused on validating satellite altimetry products in the Baltic Sea (Liibusk et al., 2020). Taiwan has strengthened regional monitoring through GNSS reflectometry applications in major ports, and Russia has contributed via coastal monitoring collaborations with China (Lee et al., 2019). France, through the Copernicus program, has played a crucial role in producing global estimates of sea level rise and developing indicators to inform climate change mitigation and adaptation strategies (Legeais et al., 2021). China and the United States are the most active contributors overall. Portugal, Estonia, Taiwan, Russia, and Mexico, on the other hand, only contribute through studies on their specific regions.

Remote sensing applications for measuring sea-level rise

The fifteen reviewed studies collectively demonstrate the diverse applications of remote sensing and geodetic methods for monitoring sea level rise across global, regional, and local scales. At the global scale, satellite altimetry has provided the foundation of sea level observation. Reprocessed datasets from TOPEX/Poseidon, Jason-1/2/3, and Sentinel missions, often combined with GRACE and GRACE-FO gravimetry, consistently report a global mean sea level (GMSL) rise of approximately 3.1–3.4 mm/yr with acceleration ranging from 0.08 to 0.12 mm/yr² (Legeais et al., 2021; Guérou et al., 2023; Cazenave et al., 2019; Hamlington et al., 2023). The methodological frameworks and specific limitations of each study are systematically detailed in Table 4.

At the regional scale, the integration of altimetry with GNSS, tide gauge, and buoy observations has improved the validation of satellite products and the characterization of spatial variability. Liibusk et al. (2020) validated Sentinel-3 sea level products in the Baltic Sea and inland waters, showing sub-decimeter accuracy in open coastal areas but reduced performance in inland lakes. Yang et al. (2019) combined tide gauges, GNSS, and altimetry to distinguish stable from subsiding islands in Hawaii, while Zhao et al. (2021) applied DInSAR and multi-sensor integration to assess sea-level rise vulnerability in China’s deltas and Saint Petersburg, achieving millimetric accuracy. Wang et al. (2020) constructed a stable Gulf of Mexico geodetic reference frame (GOM20) using long-term GNSS data, which supported more reliable estimates of coastal subsidence and sea-level rise in the region. Several studies at the local scale focused on site-specific monitoring approaches. By estimating sea level shifts at Taiwanese harbors using GNSS-IR and SNR data, Lee et al. (2019) reported centimeter-level accuracy at coastal stations. Other local validations explored how environmental and spatial factors, such as cloud interference, biases in geophysical corrections, and radar altimeter performance close to the coast, affect data reliability. (Liibusk et al., 2020; Hamlington et al., 2023). Figure 2 provides a schematic example of a subsiding coastal system (the Gulf of Mexico) showing how a moderate regional altimetry trend can translate into extreme effective relative sea-level rise when combined with subsidence.

Figure 2. Case study panel illustrating why integrating global and local observing systems is essential for coastal risk.

Example region: Gulf of Mexico. (a) Schematic altimetry-based regional sea-level trend (absolute sea level). (b) Schematic InSAR/GNSS-derived subsidence pattern (vertical land motion). (c) Schematic effective relative sea-level rise proxy combining (a) and (b) to highlight hotspots of the greatest locally experienced rise. Note: Panels are conceptual and intended to synthesize the reviewed evidence rather than reproduce a specific dataset.

These studies demonstrate how different monitoring methods complement one another across these scales. While GNSS networks and reference frames facilitate the monitoring of vertical land motion and relative sea level at regional scales, satellite altimetry and gravimetry offer reliable long-term global records.

Variables used in remote sensing methods for measuring sea-level rise

Sea-level fluctuations have been recorded through a variety of remote sensing methods, with numerous studies focused on different variables. Sea surface height (SSH) and sea level anomaly (SLA) data that have been adjusted for atmospheric effects, tides, and inter-mission biases are typically used in satellite altimetry studies (Cazenave et al., 2019; Legeais et al., 2021; Guérou et al., 2023; Hamlington et al., 2023; Biguino et al., 2024). To explain the observed variations in sea level fluctuation, researchers also use climate indicators such as the North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Niño Southern Oscillation (ENSO). Gravimetry from GRACE (Yang et al., 2019; Hamlington et al., 2023) provided ocean mass change variables, often combined with steric components from temperature and salinity or Argo floats. GNSS-based studies (Frederikse et al., 2017; Hawkins et al., 2019; Lee et al., 2019; Wang et al., 2020) emphasized vertical land motion (VLM), reference frame stability, signal-to-noise ratio (SNR), and tidal harmonics to refine relative sea-level estimates. InSAR and DInSAR applications (Zhao et al., 2021; Tang et al., 2021) have contributed localized variables, such as land subsidence rates, vertical displacement, and deformation fields, which are often cross validated with tide gauges and GPS.

At regional scales, multi-sensor frameworks (Frederikse et al., 2017; Cha et al., 2021; Borile et al., 2025). Integrated altimetry, tide gauges, GNSS, GRACE, and reanalysis products to partition relative and absolute sea-level components. Key variables included steric height, ocean mass, hydrological fluxes, and climate oscillation indices, allowing for the attribution of spatial and temporal variability.

Impact of sea-level rise

Studies consistently show that sea-level rise produces measurable yet uneven effects across scales. Globally, satellite altimetry confirms a persistent increase in global mean sea level of ~3.1–3.4 mm per year since the early 1990s, with signs of acceleration driven by ocean heat uptake and ice mass loss, and modulated by climate variability such as ENSO and the Pacific Decadal Oscillation (Cazenave et al., 2019; Cha et al., 2021; Legeais et al., 2021; Guérou et al., 2023; Hamlington et al., 2023). Regionally, circulation and hydrological processes create contrasting trends: acceleration in the western Mediterranean versus slowdown in the east, seasonal to multidecadal anomalies along the Iberian coast linked to NAO and AMO, and variability along the U.S. and Pacific coasts influenced by steric and ocean mass changes (Frederikse et al., 2017; Yang & Francis, 2019; Biguino et al., 2024; Borile et al., 2025). Locally, vertical land motion amplifies hazards, with subsiding areas such as the Gulf of Mexico, Tianjin, Asian deltas, and Taiwanese harbors showing higher relative rise and greater risks of inundation and infrastructure damage (Hawkins et al., 2019; Lee et al., 2019; Wang et al., 2020; Tang et al., 2021; Zhao et al., 2021).

Strengths and limitations of remote sensing in sea-level rise measurement

Remote sensing methods have been widely applied to monitor sea-level change at global, regional, and local scales, with complementary strengths and limitations. Satellite altimetry provided consistent estimates of global mean sea level (GMSL) and long-term trends, especially with improved corrections (Legeais et al., 2021; Guérou et al., 2023; Hamlington et al., 2023; Cazenave et al., 2019; Biguino et al., 2024). Nevertheless, performance remains limited in coastal and inland waters due to radar contamination and signal loss (Liibusk et al., 2020). Gravimetry from GRACE has improved the attribution of ocean mass changes (Yang et al., 2019), but its coarse spatial resolution and sensitivity to glacial isostatic adjustment introduce regional uncertainties. Multi-sensor frameworks combining altimetry, GNSS, tide gauges, and GRACE have enhanced the separation of relative and absolute signals (Frederikse et al., 2017; Yang et al., 2019), although results varied with processing strategies and record length.

At regional scales, integrated datasets helped attribute variability, such as Mediterranean trends (Borile et al., 2025) and continental Bayesian inversions (Hawkins et al., 2019). However, assumptions of linearity and inconsistencies in the dataset constrained accuracy. GNSS observations have improved vertical land motion estimates (Wang et al., 2020; Frederikse et al., 2017), but their accuracy depends on the network distribution and long-term stability. In contrast, GNSS-IR/SNR offers cost-effective monitoring, albeit with sensitivity to site conditions and tidal range (Lee et al., 2019). InSAR and DInSAR detected localized subsidence with millimetre precision (Zhao et al., 2021; Tang et al., 2021), but atmospheric delays, decorrelation, and computational complexity reduced robustness. Finally, hybrid analyses integrating altimetry, GRACE, tide gauges, and Argo revealed decadal variability linked to ENSO and PDO (Cha et al., 2021), though uncertainties remain in early ocean heat content records.

Effectiveness and multi-level integration of remote sensing methods

Research indicates that remote sensing is the most effective technology for monitoring sea level rise (SLR) when applied through an integrated, multi-level approach that addresses global, regional, and local scales. No single method captures the full complexity of SLR from the open ocean to the coast; therefore, a complementary suite of technologies is required to resolve the trade-offs between global precision and local relevance. A visual summary of the accuracy and scalability comparisons for each remote sensing method is presented in Figure 3.

Figure 3. Comparison of accuracy and monitoring scale of remote sensing technologies for sea level rise.

For global-scale monitoring, satellite altimetry (specifically the TOPEX/Poseidon, Jason, and Sentinel series) serves as the primary instrument for determining Global Mean Sea Level (GMSL) and regional trends, providing precise data on long-term eustatic trends (~3.3 mm/yr) and accelerations driven by thermal expansion and ice melt (Guérou et al., 2023; Legeais et al., 2021). While offering high accuracy for global averages with a trend uncertainty of approximately 0.3–0.4 mm/yr, altimetry accuracy degrades significantly within 10–20 km of the shoreline due to land signal contamination and waveform distortion (Liibusk et al., 2020; Legeais et al., 2021). To complement these geometric measurements, GRACE and GRACE-FO missions measure changes in the Earth’s gravitational field to quantify the mass component of sea level rise (caused by ice melt and terrestrial water storage changes) with an accuracy of 0.2–0.3 mm/yr and a spatial resolution of approximately 300 km, essential for closing the global sea-level budget (Cazenave et al., 2019).

At the regional level, sea-level variability is heavily influenced by ocean circulation dynamics and water budgets, often causing deviations from the global trend. Studies in the Northwest Atlantic and the Mediterranean demonstrate that fusing satellite altimetry with oceanographic data (such as steric height from Argo floats) and reanalysis models is essential to distinguish long-term climate trends from decadal variability caused by mass redistribution and steric effects (Biguino et al., 2024; Borile et al., 2025; Frederikse et al., 2017). However, on a local scale, monitoring shifts toward Relative Sea Level (RSL), where impacts are frequently dominated by Vertical Land Motion (VLM). In densely populated deltas and coastal zones, such as Tianjin and the Gulf of Mexico, subsidence rates driven by groundwater extraction can exceed global sea-level rise rates by an order of magnitude (Tang et al., 2021; Wang et al., 2020). Addressing this complexity requires an integrated approach that combines altimetry with Global Navigation Satellite System Reflectometry (GNSS-R), Interferometric Synthetic Aperture Radar (InSAR), and tide gauges to resolve high-resolution spatial variability (Hawkins et al., 2019; Lee et al., 2019; Tang et al., 2021). In this synergistic framework, GNSS serves as the “gold standard” for providing a stable geodetic reference frame and point-specific VLM calibration, while InSAR is utilized to detect ground surface deformation patterns such as subsidence or uplift across large areas with high spatial resolution (Tang et al., 2021; Wang et al., 2020; Zhao et al., 2021).

A distinct trade-off exists between global precision and local relevance. Satellite altimetry provides the global consistency necessary for understanding climate forcing and absolute sea-level trends (Guérou et al., 2023; Legeais et al., 2021) but often lacks the resolution required for local hazard mitigation due to coastal signal contamination (Hamlington et al., 2023; Liibusk et al., 2020). Conversely, tide gauges and GNSS offer high local relevance for assessing flood risk and Vertical Land Motion (VLM) but suffer from sparse and uneven spatial coverage (Hawkins et al., 2019; Lee et al., 2019). To bridge this gap, future research must prioritize extending coastal time series and using integrated approaches such as “virtual tide gauges” that fuse altimetry, GNSS, and InSAR to accurately decouple global climate signals from local ground deformation (Hawkins et al., 2019) integrated approaches such as “virtual tide gauges” that fuse altimetry, GNSS, and InSAR to accurately decouple global climate signals from local ground deformation (Hawkins et al., 2019). The most effective comprehensive monitoring strategy relies on this synergy: utilizing altimetry for global absolute trends (Legeais et al., 2021; Tang et al., 2021), GNSS and InSAR to resolve high-resolution local VLM and subsidence components (Tang et al., 2021), and GRACE/GRACE-FO to quantify the mass-driven components, such as ice melt, within the global budget (Cazenave et al., 2019; Yang & Francis, 2019).

Adaptation policy support

Accurate monitoring of sea levels is the starting point for validating and predicting the future dynamics of the sea. Multi-scale data, including global sea levels, are important for designing adaptive and effective measures for the future. This means that the effectiveness of planning for sea-level adaptation depends entirely on the accuracy and availability of high-quality sea-level monitoring data, such as that provided by the Copernicus initiative (Legeais et al., 2021; Guérou et al., 2023; Biguino et al., 2024). The Dynamic Adaptation Pathways (DAPPs) tool provides an effective policy for managing delay factors in decision-making by implementing stepwise actions based on signals from continuous sea-level monitoring. Remote sensing data plays an important role as a sea-level monitoring tool, providing continuous spatial and temporal information that complements the inherent spatial disparity in locally measured tides. Remote-sensed information provides an effective means for quantifying and evaluating sea-level dynamics and thresholds, thereby reducing epistemic uncertainties in the timing of sea-level adaptations (Hamlington et al., 2023).

The complexity of sea-level rise (SLR) observed in the coast requires an integrated and interdisciplinary approach, which requires the involvement of various sectors such as climate science, earth sciences, and social sciences, to address policy-making decisions (Legeais et al., 2021; Borile et al., 2025). Apart from data integration, stakeholder engagement is an important factor in the reduction strategy. These factors and others above explain the combination and mutual interaction between technology, data, and the collaborative efforts of society for the proper conversion and use of scientific information for effective adaptations for policy makers. Although there are many benefits derived from the use of remote sensing data, there are many challenges involved, specifically the difference between the complexity of the raw data and the use and applicability of the information. In order to address the problem, there is therefore a need for the development and use of easily accessible derivative data sets, such as the use of the Copernicus Services and the appointment of experts who are utilized for the translation of signals into warning messages (Hamlington et al., 2023). The development and maintenance of homogeneity, continuity, and error characteristics are important for supporting data policy and observation of sea-level rise. This represents the backbone of sustainable development, as observed by the United Nations (Cazenave et al., 2019). Encountered water transport and the development of the budget for surface water require observation and continuous monitoring, which necessitate advanced assessment of significant threats and impacts to inform climate adaptation.