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.

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications. An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40–70%, affecting reflectance in the red (–0.01 to 0.02) and near-infrared (NIR) bands (–0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown, reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10–20% with changes in aerosol optical thickness, 15–30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

Since the release of Meta’s Segment Anything Model (SAM), a large-scale vision transformer generation model with rapid image segmentation capabilities, several studies have been conducted to apply this technology in various fields. In this study, we aimed to investigate the applicability of SAM for water bodies detection and extraction using the QGIS Geo-SAM plugin, which enables the use of SAM with satellite imagery. The experimental data consisted of Compact Advanced Satellite 500 (CAS500)-1 images. The results obtained by applying SAM to these data were compared with manually digitized water objects, Open Street Map (OSM), and water body data from the National Geographic Information Institute (NGII)-based hydrological digital map. The mean Intersection over Union (mIoU) calculated for all features extracted using SAM and these three-comparison data were 0.7490, 0.5905, and 0.4921, respectively. For features commonly appeared or extracted in all datasets, the results were 0.9189, 0.8779, and 0.7715, respectively. Based on analysis of the spatial consistency between SAM results and other comparison data, SAM showed limitations in detecting small-scale or poorly defined streams but provided meaningful segmentation results for water body classification.

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.

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.

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.

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.

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.

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.

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.