For utilization of satellite images, enhancing the geometric accuracy and the provision of accurate ground control points (GCPs) are essential. However, maintaining and updating numerous GCPs is time-consuming and costly, presenting considerable limitations. To improve these challenges, this study proposes an automated method to accurately adjust GCP height values using rational function model (RFM) bundle block adjustment with multiple satellite images. Tiepoints over multiple images were constructed through automated matching between satellite images and GCP chips. We converted true GCP height values into heights with errors. The GCP height values were iteratively adjusted through bundle block adjustment using tiepoints over multiple images. The estimated height values were compared with the true GCP height values. Experiments compared and analyzed the accuracy of height value adjustments based on the number of satellite images used, imaging geometry, and the weights assigned in the model. Results with 13 high-resolution images showed that the root-mean-square-error (RMSE) of GCP height values improved from 8.959 m to 0.863 m after adjustment, achieving an accuracy within 1 m. Moreover, as the number of satellite images used in the bundle adjustment increased, the RMSE gradually decreased, leading to more accurate estimations. When using satellite image datasets with diverse imaging geometries, the RMSE was 0.931 m, whereas datasets with similar imaging geometries resulted in RMSEs of 1.228 m and 1.473 m, indicating lower adjustment performance. The optimal weight setting involved assigning lower weights to the initial GCP heights compared to other parameters, allowing for more significant adjustments. We highlight that tiepoints over multiple images were constructed through automated matching between satellite images and GCP chips. This supports strongly the automated update of GCP’s ground coordinates precisely. Experiment results indicated that the proposed method could be effectively utilized for practical GCP management and that it improves the quality of GCPs in areas where accurate field surveys are challenging.

Subglacial lakes, located beneath ice sheets, significantly influence ice dynamics, making the detection of their distribution and the monitoring of their fill and drain activities essential. This study focuses on the high-latitude regions near the Vostok subglacial lake in East Antarctica, an area highly probable for the presence of undiscovered subglacial lakes. We constructed two interferometric pairs of Advanced Land Observing Satellite-2 (ALOS-2) Phased-Array L-band Synthetic Aperture Radar-2 (PALSAR-2) Scanning SAR (ScanSAR) images with a 140-day temporal baseline and applied differential interferometric SAR (DInSAR). A closed circular DInSAR fringe pattern was observed near the Vostok subglacial lake. The DInSAR phase for the region can reflect displacements caused by ice sheet flow and ice surface elevation changes due to changes in the lake’s water level. To reduce the influence of ice flow and estimate the temporal differences in ice sheet elevation changes, we performed a double DInSAR (DDInSAR) technique using the two DInSAR images. The DDInSAR image showed a clearer observation of the closed circular fringe pattern detected in the DInSAR images, and the ice surface elevation observed by the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) within the fringe area showed distinct changes compared to outside the fringe area. This indicates that the circular closed fringe pattern is attributed to changes in the water level of the subglacial lake and the temporal differences in these changes. Although a subglacial lake near the discovered lake has been reported, no surface displacements that could be attributed to changes in the water level of the lake were observed in the ALOS-2 DInSAR and DDInSAR images. Both the discovered subglacial lake and the nearby reported lake exhibited slow rates of water level change. The subglacial water pathways estimated from the hydraulic potential suggest that both lakes are likely to be filled by basal melting of the ice sheet, and the drained water could flow into the Vostok subglacial lake. This study demonstrates the utility of the ALOS-2 ScanSAR interferometry for detecting active subglacial lakes with minimal activity over long periods in the high-latitude inland regions of Antarctica.

This study conducted multi-sensor image classification by utilizing Google Earth Engine (GEE) and applying satellite imagery from Compact Advanced Satellite 500-1 (CAS500-1), Sentinel-1, and Sentinel-2. The Land Use and Land Cover classification was performed using the Random Forest algorithm provided by GEE. The study experimented with various combinations of input data, integrating CAS500-1, Sentinel-1, and Sentinel-2 imagery with Normalized Difference Vegetation Index (NDVI) data from CAS500-1. The study area focused on the Goryeong County region in Gyeongsangbuk-do, and the satellite imagery was acquired in early January 2023. The results of this study showed that the highest classified result (94.51%) in overall accuracy and Kappa coefficient (0.9342) were achieved when applying CAS500-1, Sentinel-1, Sentinel-2 imagery, and NDVI data. The NDVI data is believed to complement the CAS500-1 imagery, improving classification accuracy. This study confirmed that applying multi-sensor data can improve classification accuracy, and the high-resolution characteristics of CAS500-1 imagery are expected to enable more detailed analyses within GEE.

Due to declining fish catches caused by rapid climate change and advancements in aquaculture technology, the global demand for aquaculture products is continuously increasing. However, since the reckless expansion of facilities adversely affects the coastal ecosystem and fish stock prices, managing aquaculture facilities through periodic coastal environment monitoring is essential. This study analyzed the detection accuracy of shellfish aquaculture facilities in Gyeongsangnam-do using Sentinel-2 optical imageries and deep learning-based detection methodology. The DeepLabv3+, ResUNet++, and Attention U-Net networks were applied, and as a result, Attention U-Net showed the best detection performance with F1 score of 0.8708 and Intersection over Union 0.7708. The detection methodology presented in this study allows periodic observation of aquaculture facilities affected by sea currents and suspending matters. Also, it may apply to detecting various aquaculture species, showing high potential for expansion to wider areas. Therefore, the aquaculture facility information derived through this study is expected to be useful for future policy decisions regarding marine spatial utilization.

Land use constantly changes due to climate change and human activities. Above all else, monitoring forests is crucial for global carbon management, particularly in the context of climate change. Land use changes in forested areas occur due to various factors such as development projects or natural disasters; forest fires are one of the primary drivers of large-scale forest loss. Therefore, it is essential to detect forest changes including forest fires accurately and to develop an automated system for periodic monitoring. In response, this study proposes a machine learning-based method for automating forest change detection using multi-temporal medium-resolution satellite imagery. As a case study area, the Dogye-eup, Samcheok was selected which experienced rapid forest change following significant forest fires in 2017. To construct spatial datasets for the factors influencing forest changes, key spectral bands were extracted after preprocessing the satellite images, and indices such as the normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were computed. Additionally, slope values were derived from digital elevation model (DEM) data to further enhance the dataset. Using the training set based on NDVI derived from a forest map and single-season imagery, a forest probability map was generated through a machine-learning model based on artificial neural network (ANN). The final estimate of forest reduction was determined by analyzing seasonal imagery differentials and their summation. This automated approach to extracting training data from satellite imagery and pre-existing datasets offers significant potential to enhance the automation of forest monitoring.

The Korean government plans to launch the CAS500-4 specialized for efficient management of the land. The CAS500-4 enables periodic monitoring of the Korean Peninsula with a spatial resolution of 5m along a 120 km swath width and a short revisit cycle of 3 days. To increase the usability of the CAS500-4, a process of removing geometric distortion is necessary. For this process, ground control points (GCPs) are used as key data. Acquiring GCPs through a field survey takes time and cost and cannot be achieved for inaccessible and overseas areas. In this study, we propose a method to automatically extract GCP based on Sentinel-2 Level-1C (L1C) images and generate CAS500-4 ortho-images using Rapideye 1B images with specifications like the CAS500-4 images. The proposed method is performed by generating GCP and ortho-images generation through geometric correction. After verifying the accuracy of the generated ortho-images, the sensor model accuracy before and after the geometric correction was analyzed. As a result, the sensor model accuracy with an error of less than 2 pixels was confirmed in all experimental images. Therefore, the proposed method is possible to automatically acquire GCP, it is expected to be utilized in the generation of ortho-images of CAS500-4 images in the future.

Estimation of crop-specific cultivation area is a fundamental indicator in agricultural policy decision-making, as it helps to determine the production volume for a given year. For efficient surveying of large areas, remote sensing technologies using satellite imagery and unmanned aerial vehicle (UAV) imagery are increasingly being utilized for crop-type classification. In South Korea, where smallholder farms with small fields are predominant, the low spatial resolution of satellite images poses challenges, prompting the use of UAV imagery with higher spatial resolution. Deep learning-based crop type classification, particularly through pixel-based classification methods such as semantic segmentation, faces issues like highly imbalanced class distribution and spectral correction errors caused by vignetting when mosaicing drone images. The proposed methods address the multi-class crop type classification in UAV imagery by approaching it from a parcel-based image classification perspective. By combining outputs from the Segment-Anything model with predefined smart farm maps that represent the agricultural parcel boundaries nationwide, the method successfully identifies the actual agricultural parcel boundaries in the given imagery that were not fully aligned with the vector maps. Parcel-based crop type classification was performed by assigning each image to a single class within the identified parcel boundaries. Performed on the publicly opened dataset with a differently designed form which are semantic segmentation and image classification each, the experiments show that the method has a promising increase of mean Intersection over Union performance. The results suggest that the proposed parcel-based crop type classification for UAV imagery effectively alleviates the imbalance distribution among crop classes, which is observed in semantic segmentation approaches.

As global warming accelerates greenhouse gas emissions, the frequency and severity of abnormal weather events such as floods and droughts are increasing, complicating disaster management and amplifying socio-economic damage. In response, effective strategies for mitigating water-related disasters and proactively addressing climate change are essential, which can be achieved through the use of satellite imagery. This study aims to compare the water body detection performance of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery using the Attention U-Net model. Through this comparison, the study seeks to identify the strengths and limitations of each satellite imagery type for water body detection. A 256 × 256-pixel patch dataset was developed using multi-temporal imagery from the Han River and Nakdong River basins to reflect seasonal variations in water bodies, including conditions during wet, dry, and flood seasons. Additionally, the study evaluates the impact of data augmentation techniques on model performance, emphasizing the need to select augmentation methods that align with the specific characteristics of SAR and optical data. The results demonstrate that Sentinel-1 SAR imagery exhibited stable performance in detecting large water bodies, achieving high precision in defining water boundaries (Intersection over Union [IoU]: 0.964, F1-score: 0.982). In contrast, Sentinel-2 optical imagery achieved slightly lower accuracy (IoU: 0.880, F1-score: 0.936) but performed well in detecting complex water boundaries, such as those found in wetlands and riverbanks. While data augmentation techniques improved the performance of the Sentinel-1 SAR dataset, they had only a marginal effect on Sentinel-2 optical imagery, aside from slight improvements in boundary detection under new environmental conditions. Overall, this study underscores the importance of threshold and satellite imagery integration for water body monitoring. It further emphasizes the value of selecting appropriate data augmentation techniques tailored to the characteristics of each dataset. The insights from this study offer guidance for developing enhanced water resource management strategies to mitigate the impacts of climate change.

The purpose of this study is to quantitatively and spatiotemporally analyze the effects of cool roof installations on mitigating urban heat island (UHI) phenomena. By utilizing unmanned aerial vehicle (UAV) and thermal infrared sensor (TIR), the reduction in land surface temperature (LST) due to cool roofs, a key heat mitigation measure, was analyzed across different times of the day. The research was conducted in Jangyu Mugye-dong, Gimhae-si, Gyeongsangnam-do, where cool roofs were implemented as part of a pilot project aimed at mitigating UHI effects. High-resolution thermal images were captured at two-hour intervals from 9 AM to 5 PM on a clear day using UAVs, and the spatiotemporal distribution of LST was analyzed in detail using box plots and z-scores. The results revealed that the cool roof exhibited the most significant temperature reduction effect during the morning hours (9 to 11 AM). The time with the greatest temperature difference, based on the second quartile (Q2), was 11 AM, where the cool roof’s LST was 10.52°C lower than that of a conventional roof. Conversely, this difference decreased as the afternoon progressed, reaching the smallest temperature difference of 0.04°C at 5 PM. The spatial trends of LST between cool roofs and conventional roofs were analyzed using a box plot and z-score analysis for each period. Additionally, roof objects with extreme LSTs—classified as those with values beyond the absolute range of 1.65—were identified, and the frequency of such objects was determined for each period. The analysis showed that cool roof areas consistently maintained an LST that was on average 5–10°C lower than that of conventional roofs. The highest LST observed for conventional roofs peaked at 66.02°C at 11 AM, while the lowest LST for cool roofs was 35.22°C, showing a substantial difference of approximately 30.80°C. This study presents a case demonstrating that the application of cool roofs is an effective strategy for mitigating urban heat island effects. By analyzing the temporal LST patterns in the study area and assessing z-scores for individual roof objects, the research highlights the effectiveness of cool roofs, particularly in the morning when solar radiation is lower. The findings of this study can be utilized for the broader application of heat mitigation facilities, optimal installation, and management strategies, as well as further research on effective urban heat island reduction techniques.

The importance of growth and water management for open-field crops is increasing due to climate change. Although automatic irrigation systems based on soil moisture sensors are effective for water management, they have limitations in spatially representing the entire field. To supplement this, drone imagery can be utilized. In this study, we evaluated the response of outputs based on RGB, multispectral, and thermal imagery according to the growth stages and water status of Chinese cabbage. The normalized difference vegetation index (NDVI) was useful for monitoring the initial growth stage of cabbage, while the normalized difference red edge index contributed to a more detailed assessment of the cabbage’s growth status by reflecting chlorophyll content. Plant height, estimated through the crop height model, showed the growth status during the bulbing stage under different irrigation treatments more clearly than NDVI and the height of the Chinese cabbage consistently irrigated under dry weather conditions was taller. The vegetation index and plant height from drone imagery effectively detected spatial variations within the same treatment as well as growth differences between plots with and without irrigation. The crop water stress index, derived from drone thermal imagery, immediately reflected changes in Chinese cabbage water stress after irrigation and rainfall. These results are expected to contribute not only to the utilization of various products observed by drones but also to the growth and water management for open-field Chinese cabbage farming.

Extracting building information from Very-High-Resolution (VHR) satellite images is critical for urban mapping and monitoring. Traditional manual annotation methods are labor-intensive and costly, making automated solutions highly desirable. Segment Anything Model (SAM), a foundation model trained mostly on natural images, has recently shown high performance on diverse segmentation tasks. However, due to differences in perspective and the average size of objects in the images, SAM exhibits lower performance when extracting buildings from satellite imagery. These limitations, derived from differences in image domains, can be addressed by fine-tuning the model with satellite images and preprocessing the input images. However, various hyperparameters, such as learning rate, batch size, and optimizer type, deeply impact the performance of the fine-tuned model, and thus, in-depth investigations on these hyperparameters are critical for model adaptation. To identify the optimal hyperparameter configuration, we conducted extensive experiments with combinations of hyperparameter settings using Korea Multi-Purpose Satellite (KOMPSAT) images. Additionally, various upscaling methods and object-by-object preprocessing techniques were compared and evaluated, leading to the proposal of an effective preprocessing approach. With the optimal combination, an F1 Score of 0.862, an Intersection over Union (IoU) of 0.761, and a mean IoU (mIoU) of 0.705 were achieved using AdamW optimizer, object-by-object cropping, and 100-pixel buffering. The proposed hyperparameter optimization method in our research underscores the effectiveness of fine-tuning SAM for accurate building extraction in VHR satellite imagery, thereby enabling more reliable data interpretation and decision-making processes in automated remote sensing applications.

The normalized difference snow index (NDSI) is a key indicator used to identify and map snow-covered areas by normalizing the reflectance difference between visible and shortwave infrared bands detected by satellite sensors. This study analyzed the effects of atmospheric correction on NDSI and snow cover detection characteristics according to land cover types. The study used data from the geostationary satellite (GK-2A/AMI) from November 2022 to April 2023. Comparing top-of-atmosphere (TOA) reflectance-based NDSI (NDSI_TOA) and top-of-canopy (TOC) reflectance-based NDSI (NDSI_TOC), NDSI_TOC generally showed higher values. Time series analysis revealed that the difference between the two NDSI values was relatively high when the snow-covered area was extensive. Comparison with S-NPP/VIIRS snow cover showed that NDSI_TOC-based snow detection had a higher agreement rate than NDSI_TOA-based snow detection (NDSI_TOA 72.36%, NDSI_TOC 75.88%). Analysis by land cover type showed the highest snow cover detection agreement rate in grasslands and croplands, while forest areas showed the lowest agreement rate. These findings emphasize the importance of atmospheric correction in NDSI-based snow cover detection and confirm the need for a customized approach considering land cover characteristics. This study provides a foundation for offering more reliable snow cover information in various fields such as climate change research, water resource management, aviation weather forecasting, and disaster management.

Climate change is a significant global issue recognized as being primarily caused by greenhouse gases, making it essential to understand the various factors affecting the carbon cycle for effective problem-solving. This study uses satellite data to analyze Solar-Induced Fluorescence (SIF), a key indicator of photosynthetic activity. It investigates differences across various land types based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The analysis revealed seasonal variations and higher photosynthetic indices in forested areas, suggesting the presence of robust photosynthetic activity. Additionally, this research utilized ground observation greenhouse gas data to validate the data from foreign greenhouse gas observation satellites (GOSAT, GOSAT-2, OCO-2, OCO-3) and assessed the reliability of these observations. It confirmed that the concentration of carbon dioxide (CO₂) in the atmosphere in East Asia increased by an average of about 2.68 ppmv/year from 2019 to 2023. Correlation analysis between CO₂ concentration and SIF indicated a seasonal pattern where CO₂ concentrations increased from autumn to spring and decreased during the summer, while SIF recorded the highest values in summer and the lowest in winter, showing a negative correlation. In particular, different SIF values were observed across regions, and when comparing the annual average CO₂ increase rates according to photosynthetic activity, differences based on land type were identified. These results enhance our understanding of the complex interactions between atmospheric CO₂ concentrations and terrestrial ecosystems, providing important insights into the mechanisms through which ecosystems contribute to climate change mitigation.

Coral reefs play significant roles in marine ecosystems, and recently, they have been experiencing degradation primarily due to global warming. Monitoring the coral reef ecosystem is crucial to rehabilitation and preventing further degradation. Here, we used high spatial resolution multispectral image data from the QuickBird sensor and in-situ measurements acquired around 2011 to derive a benthic habitat map around the coral reef ecosystem in Weno Island, Micronesia. Water column correction was performed to eliminate the effect of the light attenuation within the water column from the satellite image, employing a band combination approach known as the depth invariant index (DII) transformation. The combination of the new images generated by the DII transformation was used for image segmentation. This approach was conducted to apply object-based image classification. To determine the accuracy of the classification, we separate the in-situ data into 174 training data (70% of the data) and 75 testing data (30% of the data). This study produced classification results with an overall accuracy of 84% and a kappa value of 0.77, using a scale parameter of 5 for the object-based classification, which supported the reliability of the resultant coral reef habitat map. The findings of this study demonstrate that applying the depth invariant algorithm for water column correction on Weno Island is an appropriate step before conducting benthic habitat classification.

This study aims to analyze the vegetation index anomaly in the Korean Peninsula using Sentinel-2 satellite data from 2017 to 2023 and to define a visualization color scheme for vegetation index anomaly products of the CAS500-4. The study compared the impact of climate factors and local changes on vegetation index anomalies for 2022 and 2023, across different countries and land cover types. It was shown that the spring drought and abnormal temperatures of 2022 significantly impacted vegetation conditions. Additionally, the vegetation index anomaly data were statistically analyzed, and 11 color intervals were divided using the Jenks Natural Breaks method. This visualization color scheme reflects vegetation index anomaly change by climatic influences, disasters, or human activities on the Korean Peninsula. The visualization color scheme proposed in this study can serve as a reference for a future product of CAS500-4.

The continuous monitoring of water body is essential for efficient water resource management and the prevention of water-related disasters. Microsatellite and nanosatellite imageries provide a tool for continuous and accurate monitoring of water bodies at high spatial and temporal resolution. In this study, PlanetScope imagery with a resolution of 3.7 m and High-Resolution Network (HRNet) model were used to detect water bodies at dams and rivers in Korea, with the objective of evaluating the utility of water surface area monitoring. The HRNet model and the optimal band combinations of PlanetScope imagery which were R+G+B, R+G+B+NIR, Normalized Difference Water Index (NDWI), and Green+NIR+NDWI, were initially evaluated. The Green+NIR+NDWI combination performed the best, with an accuracy of 0.91 and loss function of 0.05 for the validation set. Water body detection was performed using the HRNet model with the optimal band combination and models from previous studies (Otsu, K-means, U-net) The performance was evaluated through quantitative validation using labeled images. The HRNet model showed the best performance with an Intersection over Union (IoU) of 0.96, compared to models in previous studies (Otsu: 0.90, K-means: 0.92, U-net: 0.95). Additionally, the HRNet model’s flood detection performance showed an IoU of 0.93, indicating a high accuracy. However, there were limitations, as muddy and wet soil at the boundaries of flooded areas were false detected as water bodies. In the future, when a constellation of microsatellites is developed in Korea, the results of this study are expected to contribute to better management of water resources and water-related disasters through continuous monitoring of water bodies.

We estimated the habitat suitability index (HSI) for mackerel to examine the spatial distribution of mackerel catches within the Exclusive Economic Zone (EEZ) of South Korea from 2011 to 2019, using satellite-based environmental data and the amount of catch data information provided by the government. The HSI is well-regarded for its effectiveness in identifying and predicting fishing grounds. By integrating mackerel catch data with satellite-derived environmental variables including chlorophyll-a concentration (CHL), sea surface temperature (SST), sea surface height (SSH) and primary productivity (PP), we identified optimal environmental thresholds: CHL (0.32 to 1.6 mg m^–3), SST (14.45 to 26.72°C), SSH (0.61 to 0.84 m), and PP (654.94 to 1,731.3 mg C m^–2 d^–1). Then, based on the calculated HSI, habitat suitability maps for mackerel were generated for each season, which were then compared with the distribution of catchment data in terms of validation. These results indicate that our HSI estimation is reliable for predicting mackerel fishing grounds in the South Korean EEZ. This study provides insights into mackerel’s spatial distribution patterns and environmental preferences in South Korean oceans, offering valuable information for enhancing fisheries management practices.

There are approximately 17,080 agricultural reservoirs distributed across South Korea, and maintaining these reservoirs requires significant costs and time. Accordingly, time series tracking of water surface area changes using satellite imagery has been proposed as a more efficient and economical method for managing reservoirs. This study analyzes reservoir surface areas using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) imagery and assesses the applicability of satellite data for reservoir maintenance. Sentinel-1 SAR analysis results show that except for Maengdong (0.58) and Gui (0.62), the Split and Merge technique demonstrated a correlation above 0.7. The Region Growing technique showed correlations above 0.7 for all reservoirs, while the threshold-based method maintained correlations above 0.7 for most reservoirs, except for Gui (0.68). Sentinel-2 MSI imagery was analyzed using Modified Normalized Difference Water Index (MNDWI), NDWI, and multi-band/multi-temporal NDWI approaches. MNDWI showed correlations above 0.7 only in select reservoirs, such as Maengdong, while NDWI demonstrated correlations above 0.7 for most reservoirs, except Gui (0.57) and Seongju (0.59). The multi-band/multi-temporal NDWI method exhibited correlations above 0.7 for all reservoirs except Seongju (0.52). This demonstrates the feasibility of monitoring reservoir surface area changes using satellite data and suggests its potential as a tool for supporting priority decisions in reservoir maintenance.

Satellite data are used in precision agriculture to optimize crop management. Thus, the planting pattern (e.g., flat and ridge-furrow) and crop type should be accurately reflected in the data. The purpose of this study was to identify the spatial characteristics of errors in the surface reflectance (SR) and vegetation index (VI) obtained from the Sentinel-2 satellite. Drone data were used to evaluate the suitability of the Sentinel-2 satellite for precision agriculture applications in agricultural fields. Four VIs (normalized difference vegetation index, green normalized difference vegetation index, enhanced vegetation index, and normalized difference red edge index) were calculated. The rice paddy exhibited a homogeneous surface, whereas garlic/onion and soybean fields showed high surface heterogeneity because of the presence of ridges and furrows. The SR values of the rice paddy, measured at near-infrared (NIR) wavelength using the Sentinel-2 satellite, were saturated. The VIs derived from both satellite and drone data exhibited a correlation above 0.811 and normalized root mean square error (NRMSE) below 11.1% after bias correction. The garlic and onion fields exhibited the worst results, with a bias-corrected NRMSE for VIs ranging between 12.9% and 13.8%. The soybean field, where the vegetation covered the surface almost completely, exhibited the best relationship between the Sentinel-2 and drone data. The correlation coefficient and bias-corrected NRMSE of VIs for the combination of the two devices were above 0.969 and below 6.4%, respectively. In addition, the SR at NIR had a correlation of 0.925 and a slope of 1.157, unlike in the rice paddy. These results indicate that crop structure has a greater effect than the planting pattern. The absolute difference between the VIs measured by the satellite and drone is influenced by the degree of surface heterogeneity. The errors are more pronounced at the farm-land edges. Our study contributes to a better understating of the characteristics of Sentinel-2 data for use in agricultural fields.

This study was conducted to evaluate the potential of a multimodal model for land cover classification. The performance of the Clipseg multimodal model was compared with two unimodal models including Convolutional Neural Network (CNN)-based Unet and Transformer-based Segformer for land cover classification. Using orthophotos of two areas (Area1 and Area2) in Wonju City, Gangwon Province, classification was performed for seven land cover categories (Forest, Cropland, Grassland, Wetland, Settlement, Bare Land, and Forestry-managed Land). The results showed that the Clipseg model demonstrated the highest generalization performance in new environments, achieving the highest accuracy among the three models with an Overall Accuracy of 83.9% and Kappa of 0.72 in the test area (Area2). It performed particularly well in classifying Forest (F1-Score 94.7%), Cropland (78.0%), and Settlement (78.4%). While Unet and Segformer models showed high accuracy in the training area (Area1), they exhibited limitations in generalization ability with accuracy decreases of 29% and 20% respectively in the test area. The Clipseg model required the most parameters (approximately 150 million) and the longest training time (10 hours 48 minutes) but showed stable performance in new environments. In contrast, Segformer achieved considerable accuracy with the least parameters (about 16 million) and the shortest training time (3 hours 21 minutes), demonstrating its potential for use in resource-limited environments. This study shows that image-text-based multimodal models have a high potential for land cover classification. Their superior generalization ability in new environments suggests they can be effectively applied to land cover classification in various regions. Future research could further improve classification accuracy through model structure improvements, addressing data imbalances, and additional validation in diverse environments.

The Korean Society of Remote Sensing (KSRS) has reached the 40th year since its establishment. This special issue on ‘history, status, and prospects of remote sensing in Korea’ was published to review the achievement of the KSRS and remote sensing research in Korea at the milestone of 40 years. Eleven invited papers covered major segments of remote sensing in Korea. They reviewed the development of remote sensing science and technologies over the 40 years, summarized current activities, and suggested future plans for remote sensing in Korea. We hope to provide reach backgrounds and valuable insights on remote sensing science and technologies in Korea.

Science and technology are advancing at an unprecedented pace, particularly space technology, where private sector-led innovations, including Earth Observation (EO) satellites, are driving rapid growth in the New Space era. The Korean Society of Remote Sensing (KSRS) has been pivotal in developing domestic remote sensing technology over the past 40 years publishing numerous high-quality research papers in the Korean Journal of Remote Sensing (KJRS). The Korea Multi-Purpose Satellite (KOMPSAT) series, developed under the Master Plan for the Promotion of Space Development, acquires high-resolution optical, Synthetic Aperture Radar (SAR), and Middle-Wave Infrared (MWIR) images which are used for land and ocean surveillance, forest and agricultural management, water resources and environmental monitoring, and disaster response. In this study, we analyze the research topics related to the KOMPSAT series from the numerous papers published in the KJRS over the past 40 years.

Remote sensing through meteorological satellites plays an essential role in monitoring hazardous weather conditions, providing critical data for numerical weather prediction, and contributing to climate change studies. In Korea, research in this field began in the 1980s, with early efforts focused on utilizing foreign satellite data for weather forecasting. Significant advancements were made in the 2000s with the development of Korea’s first geostationary meteorological satellite, the Communication, Ocean, and Meteorological Satellite (COMS). This satellite marked a milestone in Korea’s independent satellite data processing and value-added product generation capabilities. The development of subsequent satellites, such as the Geo-KOMPSAT 2A (GK2A), introduced significant improvements in spatial, temporal, and spectral resolution, enabling the production of a wider array of satellite products. Furthermore, advancements in artificial intelligence, cloud computing, and data assimilation techniques have further broadened the application of satellite data, particularly in nowcasting, short-term forecasting, numerical weather prediction, and climate change monitoring. This paper reviews the historical evolution of Korea’s meteorological satellite systems, the development of data processing technologies, and the application of satellite data in various fields of meteorology and atmospheric sciences. Additionally, it explores future prospects, including the development of hybrid satellite systems and the increasing role of artificial intelligence in satellite data utilization.

Since the launch of the Geostationary Ocean Color Imager (GOCI), the world’s first geostationary ocean color satellite, in 2010, and its successor, GOCI-II, in 2020, these satellites have made substantial contributions to advancing ocean color monitoring through hourly observations, enabling real-time environmental surveillance. The GOCI series has advanced ocean color satellite missions from the research level to the operational level, supporting a range of applications in marine and atmospheric monitoring. In this study, we systematically collected and analyzed 578 research papers related to GOCI and GOCI-II published from 2005 to 2023, providing insights into academic achievements, scholarly collaborations, and evolving research trends. The number of published papers has steadily increased each year. These studies were classified into four major categories: data processing (26%), ocean (52%), atmosphere (13%), and land (9%). International papers predominantly focused on ocean studies (60%), while domestic papers emphasized data processing (42%), with ocean studies accounting for approximately 35%. Annual trends revealed that data processing studies dominated until 2011, when research on ocean, and atmosphere/land applications increased. Moreover, diurnal information was utilized in 27% of the studies, demonstrating its potential for monitoring short-term changes. The application of artificial intelligence in GOCI-related research grew from 20% in 2016 to over 50% by 2022, indicating a growing trend in the use of artificial intelligence for processing large datasets.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B) satellite, launched in February 2020, represents a pioneering milestone in air quality monitoring across East and Southeast Asia. GEMS provides hourly data on atmospheric pollutants, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), volatile organic compounds such as formaldehyde (HCHO) and glyoxal (CHOCHO), as well as aerosols, all with high spatial resolution. The Environmental Satellite Center (ESC) of the National Institute of Environmental Research (NIER) is responsible for processing, retrieving, and distributing GEMS data, offering critical insights into the transport and spatial distribution of these pollutants. GEMS data has been instrumental in analyzing significant air pollution events, such as episodes of elevated particulate matter, wildfires, and volcanic eruptions. Additionally, ongoing research projects led by ESC are focused on developing novel application techniques, including satellite data fusion, top-down emissions estimation, and nighttime pollutant detection. GEMS operates as part of a global geostationary constellation, alongside the United States’ Tropospheric Emissions: Monitoring of Pollution (TEMPO) and Europe’s Sentinel-4, enhancing both the spatial and temporal coverage of air pollutants and facilitating data sharing for quality assurance. Looking ahead, ESC aims to expand its environmental monitoring capabilities by launching a constellation of microsatellites dedicated to greenhouse gas monitoring, together with the next generation of GEMS, which will continue its air quality monitoring missions. This paper presents an overview of GEMS operations, data products, and applications while outlining future strategies for enhancing air quality monitoring and supporting environmental policies aimed at clean air and climate mitigation.

This paper provides a comprehensive review of the development of Korea’s national spatial data policy and the evolution of remote sensing technology, analyzing how their interrelationship has influenced land management and public policy formulation. Beginning with aerial photography-based national mapping in the 1960s, remote sensing technology has rapidly advanced to satellite-based data acquisition since the 1990s. This progression has been a key factor in promoting the development of spatial data infrastructure at each stage of the national spatial data policy. The study reviews the introduction and application of remote sensing technology through the first to the seventh master plans of the national spatial data policy. In particular, the integration of advanced technologies such as digital twins has significantly expanded the scope of remote sensing data utilization. The successful launch of the National Land Satellite (CAS500-1) has substantially contributed to real-time monitoring of national land, environmental change detection, and natural disaster response. As Korea moves towards the construction of a national digital twin, it is expected to play an even more critical role by providing up-to-date, high-resolution spatial data, thereby enhancing both the timeliness and accuracy of policy decision-making processes. Furthermore, the paper explores future prospects of remote sensing and spatial data, proposing policy recommendations in light of anticipated technological advancements in satellite imagery technology and digital twin applications.

Remote sensing technology has emerged as a vital tool in the agricultural sector, offering capabilities for real-time crop monitoring, yield prediction, and resource management optimization. This paper reviews the historical development, current state, and future prospects of remote sensing in agriculture, with a focus on technological advancements and their impact on agricultural productivity and sustainability. The evolution of remote sensing technology, from its initial stages of soil and geographic data collection to its integration with high-resolution satellite imagery and drone technology, has significantly enhanced precision farming. These innovations enable farmers to make data-driven decisions, improve crop management, reduce resource use, and respond effectively to challenges such as climate change and food security. In particular, the establishment of the National Agricultural Satellite Center in 2024 marks a critical milestone in Korea’s efforts to advance satellite-based agricultural monitoring. The center will play a pivotal role in collecting and analyzing satellite data to monitor large-scale agricultural regions, assess environmental changes, and provide critical information for policy-making and on-field decision-making. Additionally, the combination of satellite, drone, and AI technologies is expected to further enhance the accuracy and efficiency of agricultural monitoring and management. As agriculture faces increasing global challenges such as climate change, land degradation, and food security, remote sensing technologies offer significant potential to support sustainable farming practices. This paper highlights the importance of continued research and development, as well as international collaboration, to further refine remote sensing tools and maximize their impact on the future of agriculture. The National Agricultural Satellite Center will continue to lead efforts in data-driven agricultural innovation, contributing to both national and global agricultural resilience.

Korea’s forest remote sensing began in the 1970s with the nationwide forest resource survey using aerial photographs, during which forest-type map and national forest resource data were produced for the first time. This data served as a crucial foundation for forest restoration and management at that time. In the 1990s, Landsat Multispectral Scanner (MSS) and Landsat Thematic Mapper (TM) were used to survey forest resources across North Korea, revealing for the first time the extent of forest degradation. Since then, satellite imagery has been regularly used to monitor North Korean forests, a practice that continues to this day. Since the 2000s, high-resolution satellite imagery from the Korean Multi-Purpose Satellite (KOMPSAT) series and Satellite pour l’Observation de la Terre (SPOT), along with Light Detection And Ranging (LiDAR) technology, has enabled precise analysis of forest resources. Furthermore, digital twin technology has been applied to simulate forest resource information in 3D, enabling more accurate management. Recently, deep learning and other artificial intelligence technologies have been combined with research on forest resources, forest disasters, and forest ecosystem monitoring. A significant research focus has been on creating a carbon map that spatiotemporally assesses the absorption and emission of carbon dioxide in forests. Monitoring North Korean forests also remains a critical source of data for establishing inter-Korean forest cooperation policies. The necessity of Forest Analysis Ready Data (F-ARD) has also increased. F-ARD simplifies complex preprocessing, enhancing the utility of forest remote sensing data. The F-ARD produced by the Compact Advanced Satellite 500-4 (CAS500-4, Agricultural and Forestry Satellite) will serve as a new tool for forest analysis by providing geometrically and atmospherically corrected high-resolution satellite imagery with gap-filling capabilities. The CAS500-4 set to be launched soon, will capture daily images of the Korean Peninsula, playing a crucial role in urgent responses to forest fires and forest disaster management. This satellite is expected to significantly contribute to the management of Korean forests and responses to climate change.

As disasters become more diverse and widespread, disaster management at the national level is increasingly important. Since satellite remote sensing technology is capable of observing a wide range of areas on a regular basis, it can be effectively utilized to monitor a massive scale of disaster conditions and preemptively respond to urgent disasters. Disasters that can be managed through satellite remote sensing technology include forest fires, drought, floods, landslides, and earthquakes. This study introduces a variety of studies using satellite remote sensing and Geographic Information System (GIS) as well as a number of actual cases utilizing the above. However, with the satellites currently in operation, there are difficulties in detecting and analyzing the disasters above due to the limitations on spatial and temporal resolutions. Recently, new measures have been developed to overcome such limitations through the development and constellation operation of microsatellites. In addition, new technologies are under development where a massive quantity of satellite images is analyzed by Artificial Intelligence (AI) technology. It is expected that temporal and spatial limitations can be addressed through satellite-developed and constellation systems in the future, which would lead to scientific disaster management through grafting with AI technology.

This review analyzes the application of remote sensing technologies in managing water-related disasters, specifically floods and droughts, in South Korea. As climate change increases the frequency and intensity of these disasters, effective monitoring and response systems are crucial. Remote sensing, through satellites such as optical sensors and Synthetic Aperture Radar (SAR), has become essential for disaster management, providing large-scale, real-time data. In flood management, optical satellites provide high-resolution images for assessing damage and land changes, while SAR enables all-weather monitoring, improving the accuracy and timeliness of flood response. In drought management, tools like the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, satellite rainfall data, and soil moisture monitoring contribute to early detection and long-term assessment. MODIS provides vegetation indices, such as normalized difference vegetation index and enhanced vegetation index, to track plant stress, while satellite rainfall data and soil moisture measurements offer insights into water availability. These technologies, when integrated, allow for more comprehensive monitoring of water-related disasters, reducing the risk to infrastructure, agriculture, and ecosystems. Future developments should focus on improving the resolution, speed, and accuracy of remote sensing technologies, along with enhanced data integration and collaboration between sectors to strengthen early warning systems. This review highlights the potential of remote sensing in mitigating the impacts of floods and droughts in South Korea and introduces the development and utilization of the water resources satellite equipped with a C-band SAR sensor.

This review paper provides a comprehensive overview of advancements in geological remote sensing in Korea, based on the research published in the Korean Journal of Remote Sensing (KJRS). The review encompasses critical geological domains, including lineament analysis, rock and mineral remote sensing, landslide detection, volcanic activity monitoring, earthquake assessment, and gravity studies. The content is organized chronologically, allowing for a detailed examination of the evolution of remote sensing techniques and their applications by KJRS authors. This review emphasizes significant contributions that have improved the accuracy, reliability, and predictive capabilities of geological studies through the application of remote sensing. Additionally, the paper highlights the integration of diverse remote sensing tools—ranging from satellite imagery and spectral analysis to advanced machine learning models—which collectively have facilitated a more profound understanding of geological phenomena. The insights derived from these studies are essential for the effective management of natural resources, disaster preparedness, and environmental conservation efforts.

This review analyzes the progress of polar science, emphasizing the scientific and technological achievements reflected in research papers published in the Korean Journal of Remote Sensing over the last 40 years. Polar research, particularly in the context of climate change, is a relatively young but rapidly expanding field. This review includes approximately 40 studies highlighting the application of advanced remote sensing technologies such as Synthetic Aperture Radar, multispectral, and hyperspectral imaging, LiDAR, alongside machine learning and deep learning techniques. These technologies have played a critical role in observing and analyzing the changes in sea ice and glaciers in the Arctic and Antarctic and in studying the evolving polar environment. The review covers a broad spectrum of polar research topics, including sea ice detection and classification, glacier movement tracking, atmospheric temperature estimation, and monitoring changes in ocean color and chlorophyll concentrations. Additionally, it emphasizes recent advancements in artificial intelligence methods, which have enhanced the ability to predict complex environmental changes in polar regions with greater accuracy. This review highlights the importance and potential of remote sensing technologies in driving future advancements in the field by presenting the most recent research findings related to climate change, a central issue in polar science. The 40th anniversary of the Korean Society of Remote Sensing marks a significant milestone in the history of remote sensing in Korea and the development of polar science. Over the past four decades, the society has served as a key national platform for promoting polar research through remote sensing technologies and has introduced numerous pioneering studies. In this context, this review reflects on past achievements and explores future challenges in polar science. It also provides insights into the emerging challenges the field will likely encounter. It discusses how remote sensing technologies can contribute to developing strategies to address ongoing and future changes in polar environments.