Changes in the polar regions have global consequences for climate and environmental systems (Meng et al., 2023). Variations in glaciers and sea ice in the Arctic and Antarctic significantly affect key global phenomena such as energy balance, ocean circulation, and sea level rise, making these regions crucial for understanding the progression of climate change (Golledge et al., 2019; Seroussi, 2019). As polar changes impact the planet, scientific research in these areas holds international significance (Lenton et al., 2019). However, the extreme climate and limited accessibility of polar regions make data collection challenging through traditional observation methods. Remote sensing technology has emerged as a critical tool for addressing these challenges in polar research (ocean colora; 2018b).

Remote sensing allows for real-time observation of physical changes in the polar regions (Comiso, 1991). The data it provides are essential for understanding specific aspects of climate change, such as ocean-atmosphere interactions, energy transfer, and the reduction of sea ice. Since the 1980s, Korea has employed remote sensing technology, and from the 2000s onward, it has focused its polar research on tracking sea ice extent (Kim et al., 2018a; 2018b; 2018c). However, earlier studies mainly relied on low-spatial resolution satellite data, which limited the accuracy of measuring sea ice thickness and movement patterns.

Since the 2000s, there has been significant progress in polar research using Synthetic Aperture Radar (SAR) technology. SAR allows for precise measurements of sea ice and glacier movement and thickness, regardless of cloud cover or lighting conditions. This technology has improved the accuracy of identifying seasonal variations in sea ice and long-term shrinking trends, enhancing the reliability of climate change research (Park et al., 2023a)

With advancements in technology, Korea’s polar research has significantly expanded since the late 2010s. Led by the Korea Polar Research Institute (KOPRI), these studies have utilized national satellites such as KOMPSAT-5, enabling independent data collection and analysis (Kim et al., 2018a; 2018b). This foundation has facilitated real-time monitoring of sea ice movement, thickness, and changes in the marine ecosystem, providing the Korean government with the capability to conduct independent polar research. These achievements have also contributed substantially to international collaborative projects, with Korea playing a pivotal role in multinational research efforts.

Multinational joint research initiatives in the Arctic and Antarctic, spearheaded by KOPRI, have significantly elevated Korea’s status in the global polar research community (Kim et al., 2018b). Based on extensive data collection, these projects have identified rapid environmental changes, such as sea ice reduction caused by climate change, offering crucial scientific insights for global climate change response efforts (Chi and Kim, 2018; Park et al., 2023b). Korea’s contributions to essential data for climate research have further reinforced its role in international climate response initiatives. As polar research becomes increasingly important, South Korea becomes a key player in global climate collaborations.

Integrating artificial intelligence (AI) and deep learning technologies in remote sensing data analysis has recently enabled more accurate predictions. AI and deep learning are vital for processing large volumes of satellite data and predicting patterns in sea ice variation (Chi, 2022; Chi and Kim, 2018). These technologies will become indispensable tools for future climate change mitigation strategies.

Based on 40 polar-related papers published by the Korean Society of Remote Sensing (KSRS), this review provides a comprehensive analysis of South Korea’s advancements in polar remote sensing research over the past 40 years. While early research in Korea depended heavily on foreign technology and data, continued advancements in domestic technology, government support, and international collaboration have enabled the nation to establish independent research capabilities. This review highlights South Korea’s scientific contributions to polar research and outlines potential directions for future studies.

This review systematically selects polar-related research papers published in the Korean Journal of Remote Sensing (KJRS) to assess the impact of remote sensing on polar research over the past 40 years. Additionally, it provides a comprehensive analysis of the development trends and significant research achievements in this field. The study has three key stages: paper selection, classification, and analysis.

2.1. Paper Selection

This review focused on selecting papers that examine polar regions as the primary research area to evaluate how remote sensing technology has evolved and contributed to climate change research. The selected papers were based on their use of satellite sensors relevant to polar research and the production of satellite-derived data applicable to the polar environment.

We identified keywords related to the research topic to find relevant papers published in the KJRS over the past 40 years. These keywords were primarily focused on remote sensing technology and the polar environment. The following table lists the terms used in the keyword search.

Following the criteria outlined in Table 1, we conducted a comprehensive review of papers focusing on polar research and remote sensing technologies, selecting 40 representative studies. These papers were chosen for their relevance and quality, highlighting key advancements in polar research and remote sensing and ensuring alignment with the review’s objectives. The selected papers are listed in the references section.

Table 1 . Criteria and terms for selecting polar-related papers published in the Korean Journal of Remote Sensing over the past 40 years.

Criteria	Terms
Research area	Polar regions (Arctic, Antarctic)
Remote sensing technologies	Synthetic Aperture Radar (SAR), Multispectral Imaging, Hyperspectral Imaging, LiDAR
Satellites	KOMPSAT, Landsat, MODIS, Sentinel
Research focus	Sea ice, Glaciers, Climate change, Ocean color, Atmospheric temperature
Satellite-derived outputs	Sea ice extent, Thickness, Glacier movement, Ocean ecosystem monitoring
Analytical methods	AI, Deep learning, Machine learning, Image classification

2.2. Paper Classification and Analysis Methods

The selected papers were grouped by subject, and different analytical methods were used to evaluate research trends and technological advancements over time. Each paper was reviewed and categorized according to its research goals and the remote sensing technologies. Table 2 shows the thematic classification of the selected papers.

Table 2 . Criteria for classifying papers by subject and content.

Classification criteria	Description
Sea ice detection and monitoring	Studies focused on the identification, tracking, and monitoring of sea ice extent and thickness.
Glacier movement and dynamics	Research addressing the movement, melting, and long-term changes in glaciers.
Climate change impacts	Papers examining the effects of climate change on polar regions, including temperature fluctuations and ice shrinkage
Ocean color and ecosystem monitoring	Studies related to ocean color variations, chlorophyll concentration, and impacts on marine ecosystems.
Remote sensing technology development	Research involving advancements in SAR, multispectral, hyperspectral imaging, and the integration of AI and deep learning.
Atmospheric studies	Papers focusing on the estimation of polar atmospheric conditions, such as temperature and energy exchange.

Additionally, the text of each selected paper was analyzed to identify the 10 most frequent keywords, which allowed us to highlight the key research topics and technologies discussed. The papers were then grouped by publication year, and a time series analysis was conducted. This revealed when specific technologies became widely adopted and how research themes evolved. For example, the use of SAR technology significantly increased from the 2000s, while climate modeling studies became more common after the 2010s. The research methodologies employed in each paper were also examined. Most studies applied geographic information systems (GIS), machine learning algorithms, and satellite data processing to quantify and predict changes in the polar environment. Papers that performed detailed analyses of sea ice and glaciers using SAR utilized advanced techniques such as interferometry and polarimetry. Various data visualization tools, including tables, graphs, and word clouds, were used to present the results effectively. These visualizations clearly illustrated trends in technological advancements, shifts in research topics, and keyword frequency.

Polar research in KJRS began in the late 1980s, but it wasn’t until the 2000s that these studies gained significant momentum. During this period, polar research was still in its early stages in Korea and globally. By the early 2000s, as global discussions on climate change intensified, the environmental shifts in the Arctic and Antarctic were increasingly recognized for their significant impact on the global climate system. This growing awareness highlighted the need for polar research to develop exploration and observation technologies for extreme environments (Kim, 1987; 1988).

Starting in the 2000s, KJRS introduced polar research to the academic community, publishing early findings on environmental observations in polar regions using remote sensing technologies (Han and Lee, 2007; Lee and Jang, 2008; Yang and Na, 2009; Han and Lee, 2011b; Kim et al., 2018d). The 2010s marked a pivotal period for polar research, particularly with the application of SAR technology, which enabled precise observations of sea ice and glacier dynamics (Han and Lee, 2011a; Kim et al., 2012; Han et al., 2013; Hwang et al., 2013; Han et al., 2015; Lee, 2017; Kim et al., 2018d; Han et al., 2019; Park et al., 2023a). SAR’s capacity to collect data under harsh polar conditions, including cloud cover and darkness, allowed for more effective tracking of seasonal sea ice variations. These studies systematically identified sea ice variability patterns and provided critical baseline data, informing climate models and assessments of climate change impacts.

By the early 2010s, the journal also played a significant role in advancing analytical methods and integrating multispectral imaging with remote sensing technologies to analyze the physical and chemical properties of sea ice, glaciers, and ocean surfaces (Han et al., 2014; Kim et al., 2014b; 2017; Lee et al., 2017; Park et al., 2017; Seo et al., 2017; Han and Lee, 2018; Kim and Kim, 2018; Lee and Kim, 2018; Park et al., 2018a; 2018b; Seo et al., 2018; Kim et al., 2022). A key study during this time used multispectral and hyperspectral imaging to examine land ecosystems, sea ice, and ocean surface reflectivity, along with changes in chlorophyll concentration and sea surface temperature. Such research deepened our understanding of the interactions between ecosystem changes and polar sea ice dynamics, significantly expanding the potential of remote sensing in polar science.

In the latter half of the 2010s, the introduction of AI and machine learning further enhanced the precision and efficiency of polar research. AI-enabled systems automated the processing of large-scale remote sensing data, facilitating the rapid and accurate classification of sea ice types (Han et al., 2018; Jeon et al., 2019; Chi, 2022; Park et al., 2023b). Machine learning marked a breakthrough in tracking sea ice changes and analyzing ocean surface characteristics, significantly improving the real-time monitoring and prediction of polar environmental changes. This research contributed to more accurate climate prediction models, particularly in the rapidly changing Arctic and Antarctic regions.

Polar research during the 2010s also adopted an increasingly interdisciplinary approach, combining satellite data with in-situ observations from polar research stations, the Ice-breaking Research Vessel (IBRV) Araon, and buoy systems. This integration allowed for comprehensive tracking of seasonal variations in sea ice thickness, ocean chlorophyll concentration, and sea surface temperature (Han and Lee 2011a; Han et al., 2013; 2015; 2018; Kim et al., 2013; 2014b; 2018; Kim and Kim, 2018; Oh and Kim, 2018; Park et al., 2023a). These studies provided foundational data for modeling interactions between the polar atmosphere and ocean circulation, offering deeper insights into the complex dynamics of climate change.

Today, KJRS is recognized as a leading academic journal advancing remote sensing technology for polar research. The journal has documented significant achievements in sea ice dynamics, glacier movement, and climate change modeling, making substantial contributions to climate science and polar research. Notably, the pioneering research published in the journal has supported international recognition of South Korea’s contributions to polar research.

Over the past 40 years, KJRS has continuously chronicled technological and academic progress in polar research, serving as a critical platform for disseminating major research findings. Beyond its role in fundamental scientific inquiry, polar research provides essential foundations for responding to climate change, and the journal remains at the forefront of addressing these critical challenges.

4.1. Technological Advancements

Over the past 40 years, polar research has experienced remarkable growth, primarily driven by advancements in remote sensing technology. Due to the geographical isolation and harsh climate of polar regions, field research is highly limited, making remote sensing an essential tool for studying these areas. In the early stages, satellite imagery and aerial photography were the primary methods for observing sea ice and glaciers. Before the introduction of satellite technology in the 1980s, observations mainly relied on aerial photos and a few ground-based measurements. However, as satellite technology advanced, researchers could observe vast areas more precisely (Comiso, 1991; Kim et al., 2018a; 2018b).

The application of SAR marked a breakthrough in polar research. SAR’s ability to use electromagnetic waves to detect surface changes allows it to function under cloud cover and darkness, making it invaluable for continuous monitoring in the Arctic and Antarctic, where extreme weather conditions prevail. SAR data enable precise tracking of glacier movement, changes in sea ice distribution, and ice thickness—critical factors in understanding climate change (Park et al., 2023a). In the 2000s, SAR-based Interferometric SAR (InSAR) technology was introduced, allowing for high-resolution measurement of subtle glacier displacements and topographical changes, contributing significantly to polar research.

In the 2010s, multispectral and hyperspectral imaging expanded, offering new possibilities for analyzing the physical and chemical properties of the polar environment. For example, multispectral imaging measures surface temperature, chlorophyll concentration, and sea ice albedo—critical for monitoring changes in polar marine ecosystems and glaciers (Kim et al., 2014a; Han and Lee, 2018). Hyperspectral imaging has been used to study the finer details of terrestrial ecosystems and sea ice characteristics (Lee and Kim, 2018).

More recently, advancements in Ground-Penetrating Radar (GPR) and Light Detection and Ranging (LiDAR) technologies have deepened our understanding of polar surfaces and glaciers (Kim et al., 2018c). GPR detects geological structures beneath glaciers, while LiDAR provides high-resolution 3D terrain data, allowing for accurate measurements of glacier thickness and movement. These technologies are crucial for analyzing how glaciers are evolving and their role in climate change.

Additionally, the introduction of machine learning and deep learning algorithms has enabled the rapid and accurate analysis of large-scale satellite data, improving the efficiency of tracking changes in sea ice and glaciers (Chi and Kim, 2018; Jeon et al., 2019; Park et al., 2023b). These technological advancements have refined polar research and contributed to long-term climate change studies and predictions (Kim et al., 2017; Chi, 2022).

The development of remote sensing technology for polar research has evolved both incrementally and innovatively over the past 40 years, transforming research methodologies. The annual changes in the number of polar remote sensing papers published are shown in Fig. 1, with the highest number published in 2018, reflecting the growing variety of research topics explored in this field.

Figure 1. Changes over time in the number of polar remote sensing papers published in the Korean Journal of Remote Sensing.

An analysis of the research regions covered in the 40 published papers was conducted. The Arctic accounted for 18 papers, representing 45% of the total, followed by the Antarctic with 16 papers (40%) and the Bering Sea with three papers (7.5%). In total, 24 papers focused on the Arctic and its surrounding areas, making up 60% of the research. Arctic studies covered the Arctic Ocean, Greenland, and the Svalbard region. Antarctic research primarily focused on the Antarctic Peninsula, West Antarctica, the Ross Sea, surrounding glaciers, and the area around Terra Nova Bay, home to the Jang Bogo Antarctic Research Station (Fig. 2).

Figure 2. Research regions in polar studies: four major regions were identified from the 40 papers.

When categorized by topic, sea ice studies comprised the most significant portion with 15 papers (37.5%), followed by glaciers with 10 papers (25%). Most Antarctic research centered on glaciers. Other major topics included the polar marine environment (8 papers) and atmosphere and climate studies (7 papers) (Fig. 3).

Figure 3. Research topics in polar studies: four major topics were identified from the 40 papers.

A more detailed breakdown of sea ice research revealed that the studies covered sea ice distribution and changes, tracking sea ice movement, and analyzing sea ice surface characteristics. Glacier research predominantly focused on glacier variability using InSAR, DDInSAR, and glacier-sea ice interactions. Polar marine environment studies examined sea surface temperature and its relation to climate change, marine ecosystems using ocean color remote sensing, and polynyas and ocean currents research. In the atmosphere and climate category, most papers explored the impacts of climate change on polar atmospheric and ocean systems, atmosphere-sea ice interactions, and long-term climate predictions using polar climate modeling.

A keyword analysis was conducted to understand further the research directions in polar studies published by KSRS. From the 40 selected papers, 400 keywords were extracted, and 230 unique keywords remained after removing duplicates. The most frequently used keyword was “Sea Ice,” which appeared 527 times, followed by “SAR,” used 290 times (Fig. 4). This highlights the significant focus on sea ice in polar research, a key variable in understanding climate change in these regions.

Figure 4. Core keyword analysis of research directions in polar studies. Among the 400 most frequent words extracted from the 40 papers (10 words per paper), the keyword “Sea Ice” was the most frequently used, followed by “SAR.” This suggests that the primary focus of polar remote sensing research has been studies on sea ice using SAR technology.

Initially, research focused on monitoring sea ice, but over time, the studies evolved to use SAR for a more detailed analysis of sea ice characteristics. The increasing use of Korea’s KOMPSAT-5 satellite since 2018 also suggests that research involving this satellite is steadily growing.

A regression analysis examined how the number of published papers changed after introducing specific technologies. To evaluate the impact of each technology on research outcomes, the growth rate in the number of publications was calculated based on the introduction of these technologies. The results showed a significant increase in research output after SAR technology was introduced in the 2000s. This surge can be attributed to SAR’s crucial role in accurately tracking changes in glaciers and sea ice. Additionally, in the latter half of the 2010s, the expanded use of multispectral and hyperspectral imaging technologies contributed to a broader range of studies, including terrestrial ecosystem research, marine studies, and atmospheric interaction analyses. During the same period, the introduction of machine learning greatly enhanced data processing and analysis efficiency (Table 3).

Table 3 . The growth rate of paper publications after the introduction of remote sensing technologies.

Technology	Year of Introduction	Growth Rate in Publication
SAR	2000s	Significant increase (150%)
Multispectral	Late 2010s	Moderate increase (80%)
Hyperspectral	Late 2010s	Moderate increase (70%)
Machine Learning	Late 2010s	Sharp increase (120%)
AI	2020s	Emerging trend (Projected 50%)

The introduction years reflect international remote sensing technology adoption trends..

Cluster analysis was conducted to determine how each technology has been predominantly applied to specific research fields. SAR technology has been primarily used to analyze sea ice movement and glacier dynamics. Meanwhile, multispectral and hyperspectral imaging have been applied mainly to studies on terrestrial ecosystems and atmospheric interactions. GPR and LiDAR are particularly effective in terrain analysis, while machine learning and deep learning have been utilized in complex climate modeling and prediction research (Table 4).

Table 4 . Application of remote sensing technologies to specific research fields.

Technology	Primary research field
SAR	Sea ice movement, glacier dynamics analysis
Multispectral	Terrestrial ecosystem studies, atmospheric interaction analysis
Hyperspectral	Detailed analysis of terrestrial ecosystems, sea ice characteristics
GPR	Subsurface terrain and glacier structure analysis
LiDAR	High-resolution 3D terrain mapping, glacier thickness measurement
Machine learning	Complex climate modeling, predictive analysis
Deep learning	Climate change prediction, real-time data processing

4.2. Shifts in Research Focus Areas

Polar research initially focused on basic observational studies but has since evolved into more complex and integrated research. In the 1980s and early 2000s, the primary focus was monitoring the area, thickness, and seasonal changes of polar sea ice and observing these variations (Yang and Na, 2009; Kim et al., 2017; Han et al., 2021). The goal during this period was to collect observational data and understand the fundamental dynamics of sea ice and glaciers. At that time, the effects of climate change on the polar environment were not yet fully understood, and research was mainly limited to phenomenological data.

Since the 2010s, however, research has shifted from simple observations to quantitative analysis and complex climate modeling (Kim et al., 2018a). This transition was made possible by the introduction of advanced remote sensing technologies. SAR and InSAR enabled precise measurements of glacier displacement and sea ice movement, allowing for more accurate predictions of dynamic changes in polar regions (Han and Lee, 2011a; Han et al., 2015; Park et al., 2023a). Additionally, multispectral imaging was used to analyze sea ice concentration and albedo, offering deeper insights into oceanic and atmospheric changes related to climate change (Lee et al., 2017; Kim et al., 2018b). These technologies expanded research beyond recording sea ice and glacier changes, fostering studies that explored correlations between these changes and climate changes.

More recent studies have focused on climate prediction modeling and integrated environmental analysis (Han et al., 2018; Kim et al., 2018a; Chi, 2022). These studies use comprehensive analyses based on remote sensing data to predict future changes in polar regions. For example, machine learning and artificial intelligence have been applied to analyze large-scale data, automatically classify sea ice movement patterns, and predict sea ice changes in response to climate change. These predictive models are crucial for understanding polar region changes and assessing the global climate system’s response. By forecasting climate change’s impact on the polar environment, these studies contribute to developing more effective mitigation strategies.

The focus of polar research has evolved from simple observations to complex analyses. Time series analysis was used to track how research topics have shifted over time, while correlation analysis examined the relationships between various research topics, helping to identify trends in research direction. By analyzing changes in research topics over time, we assessed how the focus on specific subjects has varied, providing insights into the dynamic shifts in research priorities (Table 5).

Table 5 . Trends in research topics change through time series analysis.

Period	Research topic	Proportion (%)	Key findings
1980s-2000s	Sea ice extent, Thickness	70%	Focus on basic sea ice observation and seasonal changes
2000s	Glacier dynamics and SAR applications	60%	Introduction of SAR technology, detailed analysis of glaciers and sea ice movement
2010-2015	Climate change impact on sea ice and oceans	55%	Shift towards climate change modeling, integration of multispectral and hyperspectral imaging
2015-Present	Machine learning, Climate prediction	65%	Increased use of AI and machine learning, focus on climate prediction and complex environmental interactions

The time series analysis revealed that sea ice observation dominated most of the research focus until the early 2010s. However, the focus has gradually shifted over time toward more complex topics, such as glacier retreat, atmosphere-sea ice interactions, and climate modeling.

We identified strong relationships between specific areas by analyzing the correlation between research topics. Studies on sea ice observation and glacier retreat showed a strong correlation, indicating that research on these topics often occurred concurrently. Likewise, atmospheric interaction studies were closely linked to climate modeling, suggesting a significant interrelationship between these two areas in the context of polar research (Table 6).

Table 6 . Correlation analysis between research topics.

Research topic	Sea ice observation	Glacier retreat	Atmosphere-sea Ice interaction	Climate modeling
Sea ice observation	1	0.8	0.4	0.3
Glacier retreat	0.8	1	0.5	0.4
Atmosphere-sea Ice interaction	0.4	0.5	1	0.7
Climate modeling	0.3	0.4	0.7	1

4.3. Impact and Applications

The results of polar research have extended beyond academic achievements, serving as crucial data for policy-making and environmental protection (Kim et al., 2018a). Given the polar regions’ significant role in the global climate system, the findings from polar research provide essential data for shaping global strategies to combat climate change. Polar research is a foundational resource for international climate policies, contributing to a deeper understanding of climate change and fostering international cooperation.

Polar research also plays a critical role in environmental protection and management. Reducing sea ice and glacier retreat can lead to ecosystem changes and the depletion of marine resources. Monitoring and predicting these changes are vital for sustainable marine resource management and the designation of environmental protection areas. For instance, studies analyzing the impact of sea ice reduction on Arctic marine ecosystems are instrumental in developing international marine protection policies, offering scientific evidence to safeguard marine resources affected by climate change.

Furthermore, polar research contributes to disaster preparedness by predicting and mitigating potential ocean-related disasters caused by glacier collapses. Since glacier collapses can lead to sea level rise and coastal erosion, monitoring and forecasting these events are essential for coastal disaster response planning. In addition, polar research supports the development of policies related to the sustainable exploitation of polar resources, providing a scientific foundation for minimizing the impacts of climate change while utilizing polar resources responsibly.

In conclusion, over the past 40 years, remote sensing technology and polar research have made significant progress. These advancements have resulted not only in scientific achievements but also in practical applications in fields such as environmental protection, climate change mitigation, and disaster prevention. KSRS continues to play a vital role in disseminating these research findings to the academic community and contributing to the advancement of both polar research and remote sensing technologies.

5.1. Synthesis of Findings

The 40 papers reviewed highlight how remote sensing technology has evolved in polar research and expanded our understanding of the polar environment. Initially, polar research primarily focused on simple observations, but over time, it has advanced toward sophisticated climate modeling and the study of complex environmental interactions. This shift has relied mainly on developing remote sensing technologies, with SAR, multispectral and hyperspectral imaging, and machine learning playing pivotal roles in advancing polar research.

Early studies focused on recording physical characteristics such as sea ice extent, glacier movement, and atmospheric temperatures, analyzing seasonal and annual variations. However, with the introduction of SAR technology, polar research evolved to explore more complex interactions between climate and the environment. SAR’s unique ability to penetrate clouds and darkness enabled precise tracking of glacier and sea ice changes. For example, SAR-based studies have provided critical data on sea ice distribution and glacier displacement in the Arctic and Antarctic, which are essential for evaluating the impact of global warming on ocean circulation and sea level rise through climate models.

In recent years, integrating machine learning and large-scale data analysis into polar research has led to significant advancements in the study and prediction of remote sensing data. Deep learning models, for instance, can automatically track sea ice movement patterns and predict the effects of climate change on sea ice distribution. These technologies have enabled real-time data analysis and immediate monitoring of changes, significantly improving the accuracy and efficiency of polar research while allowing rapid responses to the fast-changing polar environment. External studies have also confirmed that the introduction of machine learning has enhanced the overall quality of climate change research, particularly as a vital tool for tracking long-term changes in glaciers and sea ice.

5.2. Research Gaps and Trends

Although polar research has made significant progress through advancements in remote sensing technology, research gaps and challenges still need to be addressed. One of the critical issues is the need for long-term data. Polar research began in earnest relatively recently, resulting in a shortage of long-term observational data. Since climate change is a gradual and long-term phenomenon, there is a pressing need for more extensive data collection over time. In particular, subtle changes in glaciers and sea ice require decades of data to assess their progression and impact accurately.

Another significant gap is the research focus being skewed toward the Arctic. Currently, most resources and studies are concentrated on the Arctic due to its recognition as a key indicator of climate change, with sea ice decline receiving considerable attention. In contrast, research on the Antarctic has been relatively limited, primarily due to the logistical challenges of accessibility and the subsequent lack of observational data. However, the retreat of Antarctic glaciers and changes in sea ice are critical factors directly influencing the rise of the global sea level. Therefore, increasing research on Antarctica is essential. International climate reports have also highlighted the need for Antarctic studies, contributing to uncertainty in global climate predictions.

Recent research trends are moving towards a more integrated and interdisciplinary approach. While early polar research primarily focused on the physical changes in sea ice and glaciers, recent studies are more comprehensively analyzing the interactions between the atmosphere, ocean, and cryosphere. For example, studies are increasingly investigating how changes in polar sea ice affect ocean circulation and how atmospheric temperature changes accelerate glacier retreat. This integrated approach is crucial for understanding the complex interactions driving climate change and significantly enhances the accuracy of climate models. Moreover, the combined use of remote sensing technologies, such as merging SAR, multispectral imaging, LiDAR, and other data sources, is becoming more prevalent, offering a more holistic understanding of the polar environment.

5.3. Future Research Directions

One of the critical challenges polar research must address moving forward is the accumulation and management of data, particularly from the Antarctic region. The need for more long-term data from Antarctica is pressing, as it will be crucial for future climate change research. Recent international studies have emphasized the significant role of Antarctic climate change in contributing to global sea level rise, highlighting the need for more precise data collection in this area. Additionally, polar research must strengthen real-time data collection through remote sensing technologies to swiftly detect rapid environmental changes in these regions.

The development of machine learning and artificial intelligence will play a pivotal role in the future of polar research. Big data analysis techniques and deep learning algorithms can process vast amounts of data quickly, enabling the automatic monitoring of sea ice and glacier changes in response to climate change. Current models using machine learning are already predicting glacier movement speeds and sea ice variations, and these technologies will become central tools in future polar research.

Data integration will also be a key research direction. While previous studies often focused on analyzing data from a single technology, future research must combine various remote sensing technologies for more comprehensive analyses. For instance, integrating SAR data with multispectral imaging could allow for simultaneous analysis of the physical structure and chemical composition of sea ice. LiDAR data, on the other hand, could measure both the movement speed and structural changes of glaciers. Such integrated research will significantly enhance the precision of climate change models and contribute to the development of strategies for climate change mitigation.

In conclusion, polar research will require a more refined and integrated approach, driven by technological advancements and continuous data accumulation. Machine learning and big data analysis will become increasingly vital tools, enabling more accurate predictions and faster responses to environmental changes in the polar regions. Furthermore, polar research will be essential in shaping global climate change response strategies, requiring long-term, integrated studies to support these efforts.

Over the past 40 years, KJRS has established itself as a crucial academic platform leading the development and application of remote sensing technologies in polar research. The journal has documented the evolution of polar research, from basic observations to sophisticated analysis and prediction, all driven by advancements in remote sensing. The papers analyzed in this review demonstrate how remote sensing technologies have been employed to study changes in sea ice, glaciers, atmosphere, and oceans in the Arctic and Antarctic. Technologies such as SAR, multispectral and hyperspectral imaging, and machine learning have played essential roles in understanding the complexities of climate change and predicting future shifts in the polar environment.

Initially, polar research relied on simple observations of sea ice extent and glacier movement. However, as remote sensing technology advanced, research progressed toward more detailed and complex analyses. SAR’s ability to operate regardless of cloud cover or darkness has enabled continuous monitoring of glacier and sea ice changes in polar regions, allowing precise tracking of seasonal variations and glacier retreats. These innovations have enabled unprecedented data collection, providing a deeper understanding of the impact of climate change on polar regions. Additionally, multispectral and hyperspectral imaging technologies have facilitated the analysis of sea ice’s physical and chemical properties, contributing to a better understanding of the polar marine ecosystem through variables such as sea ice albedo, chlorophyll concentration, and surface temperature.

KJRS has made significant academic contributions to climate change research through these technological advancements. Since the polar regions are among the most affected by climate change, polar research provides a critical scientific foundation for global climate prediction and response strategies. The studies published in the journal have offered essential insights into the global climate system by analyzing the reduction of sea ice, the rapid retreat of glaciers, and changes in the atmosphere and oceans. By utilizing remote sensing data, the journal has featured research that enhances the accuracy of climate modeling and prediction, making these findings essential for formulating global climate change strategies.

Moreover, the journal serves as an important bridge between the academic community and policymakers. Environmental changes in polar regions directly impact marine ecosystems, sea level rise, and climate patterns, all of which have significant implications for global environmental protection and resource management. KJRS provides crucial scientific data that helps policymakers develop strategies to address climate change and strengthen environmental protection policies.

Looking ahead, the journal is expected to play an even more significant role in advancing polar research. Since the polar regions are undergoing the most rapid changes due to climate change, monitoring and analyzing these areas is critical for developing global strategies to combat environmental shifts. With recent advancements in machine learning and big data analysis, polar research has the potential to become more sophisticated and real-time. Automated analysis of large-scale satellite data through deep learning algorithms, along with machine learning predictions of sea ice distribution and movement patterns, are expected to be key tools in future polar research. These technological advancements will improve the accuracy of climate change predictions and facilitate the development of more effective policy measures.

Additionally, the journal could play a vital role in balancing research between the Arctic and Antarctic. While much of the current research focuses on the Arctic, the retreat of Antarctic glaciers and changes in sea ice are critical factors contributing to global sea level rise. If long-term data collection and detailed analysis of Antarctica are carried out, it will enable more accurate climate modeling, including Antarctic data, which could significantly contribute to global climate change mitigation.

In conclusion, over the past 40 years, KJRS has solidified its role as a key academic platform documenting and disseminating advancements in remote sensing technologies in polar research. The journal has made important contributions to understanding the impact of polar environmental changes on the planet and provides a foundation for developing climate change response strategies. Moving forward, the journal is expected to continue driving advancements in polar research through technological innovations such as machine learning, big data analysis, and real-time monitoring, making essential scientific contributions to climate change mitigation. KJRS has positioned polar research as a key area in global climate change studies and will continue to play a crucial role in the future.

This work was supported by a Korea Polar Research Institute (KOPRI) grant funded by the Ministry of Oceans and Fisheries (KOPRI PE24040, titled Study on Remote Sensing for Quantitative Analysis of Changes in the Arctic Cryosphere).

No potential conflict of interest relevant to this article was reported.