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 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.

Waterbody change detection using satellite images has recently been carried out in various regions in South Korea, utilizing multiple types of sensors. This study utilizes optical satellite images from Landsat and Sentinel-2 based on Google Earth Engine (GEE) to analyze long-term surface water area changes in four monitored small and medium-sized water supply dams and agricultural reservoirs in South Korea. The analysis covers 19 years for the water supply dams and 27 years for the agricultural reservoirs. By employing image analysis methods such as normalized difference water index, Canny Edge Detection, and Otsu’s thresholding for waterbody detection, the study reliably extracted water surface areas, allowing for clear annual changes in waterbodies to be observed. When comparing the time series data of surface water areas derived from satellite images to actual measured water levels, a high correlation coefficient above 0.8 was found for the water supply dams. However, the agricultural reservoirs showed a lower correlation, between 0.5 and 0.7, attributed to the characteristics of agricultural reservoir management and the inadequacy of comparative data rather than the satellite image analysis itself. The analysis also revealed several inconsistencies in the results for smaller reservoirs, indicating the need for further studies on these reservoirs. The changes in surface water area, calculated using GEE, provide valuable spatial information on waterbody changes across the entire watershed, which cannot be identified solely by measuring water levels. This highlights the usefulness of efficiently processing extensive long-term satellite imagery data. Based on these findings, it is expected that future research could apply this method to a larger number of dam reservoirs with varying sizes, shapes, and monitoring statuses, potentially yielding additional insights into different reservoir groups.

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.

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks (UDMs) with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

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.

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.

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.

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.