Methane from rice fields has a strong greenhouse effect and its accurate estimation is essential to combat climate change. In this study, we conducted an analysis based on the Gradient Boosting Machine (GBM) model using Local Data Assimilation and Prediction System (LDAPS) data, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) from VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS), and FluxNet ground observations of the Cheorwon rice paddy region. This was used to estimate methane emissions from rice paddy fields in South Korea and to create a gridded spatial information map of methane concentrations. Using data with a spatial resolution of 1.5 kilometers, we identified detailed changes within the region and generated daily maps to analyze daily changes and seasonal characteristics. To predict methane concentration, we considered the correlation between meteorological factors such as latent heat flux, humidity, soil moisture, and soil temperature and methane emissions as key variables. Latent heat flux and humidity were selected as key variables considering that the migration of methane gas is affected by the evapotranspiration process. In addition, soil moisture, which creates the anaerobic conditions necessary for methane production, and soil temperature, which affects the activity of methanogenic microorganisms, were included in the analysis. Taking these various factors into consideration, we analyzed methane emission data from rice fields in Korea and visualized them on a map to understand the pattern of methane production in response to changing weather conditions. The developed model showed a correlation coefficient of 0.91 and Mean Absolute Error (MAE) of 28.97 in the 5-fold cross-validation and an average correlation coefficient of 0.87 and MAE of 35.46 in the Leave One Year Out (LOYO) cross-validation. These results are expected to contribute to the understanding of methane generation patterns under changing weather conditions and accurate methane emission estimation. In addition, the developed model and the constructed methane concentration map can be utilized as an important basis for establishing greenhouse gas reduction policies in the agricultural sector and effective climate change response strategies in the future.

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.

Due to global weather anomalies and climate change, natural disasters are increasingly escalating into extreme catastrophes year by year. The intense summer rains, late autumn heatwaves, and early snowfall in central Korea in November 2024 reflect the extreme climate phenomena and the evolving paradigm of disasters. By utilizing advanced Earth observation platforms and remote sensing technologies, it is possible to effectively predict and prepare for disasters, collect timely observational data to respond in real-time during disaster management and conduct integrated analyses of large-scale disaster damage alongside existing spatial information. The special issue of the National Disaster Management Research Institute (NDMI) in 2024 introduced case studies of landslide damage assessment using remote sensing and spatial information integration, and safety inspections of facilities using multi-sensor drones. Furthermore, it focused on recent trends in research related to the fusion of remote sensing and spatial information technologies, as well as the research outcomes of the NDMI. The integrated analysis approach based on remote sensing and spatial information is meaningful for systematic disaster accident management during the prevention, preparedness, response, and recovery phases. Specifically, the fusion of remote sensing and spatial information analysis and its application are expected to play a crucial role in pre-disaster prediction, rapid response, and decision-making for minimizing damage in national disaster incidents.

Remote sensing technology has significantly enhanced crop growth and disease monitoring. While satellites operate above the clouds, Unmanned Aerial Vehicle (UAV) mostly operate below the clouds, making cloud cover a critical factor. This study aims to identify the best surface reflectance correction method for UAV images under Sky Clear (SKC) and Broken Clouds Sky (BKN) conditions by comparing NoProcess, New Row Gradient (NewROW), and Bidirectional Reflectance Distribution Function (BRDF) methods. Data was collected using a RedEdge MX camera, and the accuracy of the data results was validated by comparing them with Calibrated Reference Tarp (CRT) ground reflectance data. The RedEdge MX camera includes the Original Row Gradient (OriROW) parameter in its metadata. For users without the BRDF parameter (f_iso, f_geo, and f_vol), OriROW can be utilized after data collection. NewROW was proposed to address the limitations of existing OriROW. The NewROW formula was enhanced to consider sun position and image center values. In the SKC, NewROW and BRDF were highly accurate with correction total values of ±1.57 and ±1.68% in all bands, while NoProcess was lower accurate with correction values of ±4.02%. In BKN, NewROW, and BRDF performed well with correction values of 1.31 and 1.33%, while NoProcess was less effective at 1.83%. Comparing the NoProcess, NewROW, and BRDF under SKC and BKN, the reflectance differences were –2.414 to 1.212, –0.687 to 0.745, and –0.989 to 1.143%. Therefore, both BRDF and NewROW showed high accuracy under both clear and cloudy sky conditions, with the simpler NewROW serving as an effective alternative to BRDF.

Advances in the aerospace industry have driven growing research into the use of artificial intelligence for analyzing objects of interest in satellite imagery. Unlike typical 8-bit RGB camera images, however, satellite imagery often contains 16-bit pixel values, which can result in outliers that darken the image. This issue leads to difficulty in object identification and negatively impacts analysis performance. To address this issue, various image enhancement techniques have been proposed, but the effectiveness of each technique depends on the specifics of each satellite and the task to be performed. To address this issue, various image enhancement techniques have been proposed. However, since each satellite has unique characteristics and the effectiveness of each technique varies depending on the specific task, it is necessary to carefully evaluate which technique is most suitable. This research analyzed which of the five image enhancement techniques is most suitable for semantic segmentation tasks using the dataset from the KOMPSAT-3A satellite, which is widely used in South Korea. Experimental results using five semantic segmentation models indicated that percentile stretching performed well in three models, suggesting it as the most universally applicable method. In addition, for buildings and roads, which are important objects in urban analysis, recursive separated and weighted histogram equalization (RSWHE) and percentile stretching were found to be effective.

Maritime accidents cause human and property losses, making timely detection of small ships crucial for improving rescue operations’ efficiency. To address this, this study constructed a high-resolution training dataset for small ship detection using satellite imagery and evaluated the performance of various deep learning models. Among the detection transformer (DETR) models, the DETR with improved denoising anchor boxes for end-to-end object detection (DINO) model achieved an average precision (AP) of 0.934, outperforming convolutional neural network (CNN)-based models. Notably, it succeeded in detecting small ships under 10 meters. Furthermore, detection experiments using the BlackSky microsatellite constellation evaluated the efficiency of maritime monitoring and search and rescue operations, with positional errors between global positioning system (GPS) and detected ship locations of 64.23 m and 54.89 m in the first and second experiments, respectively. These results confirm the practicality of the proposed transformer-based high-resolution satellite imagery methodology for small ship detection and suggest potential improvements in detection performance under various maritime conditions.

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.

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.

The International Civil Aviation Organization (ICAO) emphasizes the importance of developing technologies to enhance the safety and efficiency of air traffic through trajectory-based operations (TBO). In this context, this study focuses on the NARAE-Weather system, currently under development in Korea, and its core component, the 4D-Wx Application Programming Interface (API) distribution service, to propose an approach for providing aviation weather information to support air traffic operations. The NARAE-Weather system integrates diverse meteorological data to deliver standardized weather forecasts optimized for trajectory based (4DT), regions of interest (ROI), and points of interest (POI), enabling customized aviation operation support. This paper evaluates the service scope and technical feasibility of the 4D-Wx API and outlines a direction for supporting air traffic operations through the provision of multidimensional weather information. Specifically, the study examines the effectiveness of delivering realtime weather information via the API to support trajectory-based operations.