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.

Land cover change due to urban population concentration and urban expansion can cause various environmental problems such as urban heat islands. In particular, New towns are considered an appropriate study site to analyze changes in urban climate due to rapid urbanization in a short period. This study used Landsat satellite imagery to compare and analyze the land cover changes before and after the development of two new towns with different plans, and the resulting changes in surface urban heat island (SUHI) phenomena. Correlation analysis was also conducted between urban structural features that may affect the SUHI intensity. The results of the analysis confirm the rapid change in land cover as new town development progresses and the direct intensification of the SUHI phenomenon. This study confirms the differences in SUHI caused by different urban plans and suggests the need for threedimensional urban planning to improve the thermal environment.

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.

Absolute radiometric calibration is a crucial process in converting the electromagnetic signals obtained from satellite sensors into physical quantities. It is performed to enhance the accuracy of satellite data, facilitate comparison and integration with other satellite datasets, and address changes in sensor characteristics over time or due to environmental conditions. In this study, field campaigns were conducted to perform vicarious calibration for the multispectral channels of the CAS500-1. Two valid field observations were obtained under clear-sky conditions, and the top-of-atmosphere (TOA) radiance was simulated using the MODerate resolution atmospheric TRANsmission 6 (MODTRAN 6) radiative transfer model. While a linear relationship was observed between the simulated TOA radiance of tarps and CAS500-1 digital numbers (DN), challenges such as a wide field of view and saturation in CAS500- 1 imagery suggest the need for future refinement of the calibration coefficients. Nevertheless, this study represents the first attempt at absolute radiometric calibration for CAS500-1. Despite the challenges, it provides valuable insights for future research aiming to determine reliable coefficients for enhanced accuracy in CAS500-1’s absolute radiometric calibration.

The Korea multi-purpose satellite (KOMPSAT) series consisting of multi-sensors has been used in various fields such as land, environmental monitoring, and disaster analysis since its first launch in 1999. Recently, as various information processing technologies (high-speed computing technology, computer vision, artificial intelligence, etc.) that are rapidly developing are utilized in the field of remote sensing, it has become possible to develop more various satellite image processing and analysis algorithms. In this special issue, we would like to introduce recently researched technologies related to the KOMPSAT image application and research topics participated in the 2023 Satellite Information Application Contest.

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.

Urban vitality is an important indicator for assessing the growth potential of cities and the quality of life of citizens. Its significance is increasingly recognized due to its ability to reflect the diverse characteristics of urban environments. In the case of Democratic People’s Republic of Korea (DPRK), the unique political system and social ideology are deeply embedded in urban spaces and society, making it difficult to fully understand the conditions through socioeconomic indicators alone. Therefore, analyzing urban vitality is essential for gaining a deeper understanding of DPRK. This study aims to estimate a regional-level urban vitality indicator that captures the dynamic characteristics of cities in DPRK and to analyze the country’s urban vitality based on this indicator. To achieve this, urban vitality indicators were defined using socioeconomic data from Republic of Korea, and machine learning techniques were applied to estimate the urban vitality indicators for DPRK. A qualitative analysis of DPRK was then conducted based on the estimated values of these indicators. The results of this study demonstrate that areas with higher urban vitality in DPRK are typically those where government-led developments have occurred, while regions with lower urban vitality are primarily agricultural areas, islands, and mountainous regions with limited accessibility. Furthermore, the study found that the actual nighttime power supply is insufficient when compared to the infrastructure levels in major cities of DPRK. The methodology proposed in this study can be expanded to other inaccessible regions, including the DPRK. Therefore, the urban vitality derived from this study is expected to be useful for policymaking in the DPRK and other restricted regions in the future.

Various earth observation satellites need to provide accurate and high-quality data after launch. To maintain and enhance the quality of satellite data, it is crucial to employ a cross-calibration processthat accountsfor differencesin sensor characteristics,such asthe spectral band adjustment factor (SBAF). In this study, we utilized Landsat-8 and Sentinel-2A satellite imagery collected from desert sites in Libya4, Algeria3, and Mauritania2 among pseudo-invariant calibration sites to calculate and apply SBAF, thereby compensating the uncertainties arising from variations in bandwidths. We quantitatively compared the reflectance differences based on the similarity of bandwidths, including Blue, Green, Red, and both the near-infrared (NIR) narrow, and NIR bands of Sentinel-2A. Following the application of SBAF, significant results with reflectance differences of approximately 1% or less were observed for all bands except NIR. In the case of the Sentinel-2A NIR band, it exhibited a significantly larger bandwidth difference compared to the NIR narrow band. However, after applying SBAF, the reflectance difference fell within the acceptable error range (5%) of 1–2%. It indicates that SBAF can be applied even when there is a substantial difference in the bandwidths of the two sensors, particularly in situations where satellite utilization is limited. Therefore, it was determined that SBAF could be applied even when the bandwidth difference between the two sensors is large in a situation where satellite utilization is limited. It is expected to be helpful in research utilizing the quality and continuity of satellite data.

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.

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.