Korean Journal of Remote Sensing

4D NARAE-Weather Data Platform Services for Supporting TBO

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 for...

Korean J. Remote Sens. 2025; 41(1): 1-10

Hydrological and Mathematical Framework of Ensemble Learning Regression for Estimating Discharge in Ungauged River Basins

Accurate estimation of river discharge (Q) globally is essential for water resources management, including flood control and drought management. However, the number of stream gauges globally used to calculate Q is decreasing. Additionally, estimating Q of transboundary rivers or rivers with unique hydrological characteristics, such as the Mekong River, is challenging using traditional hydrological methods. The most representative methods for estimating Q have been the empirical power function using at-a-station hydraulic geometry (AHG) proposed by Leopold and Maddock (1953) and the method usin...

Korean J. Remote Sens. 2025; 41(1): 11-29

Daytime and Nighttime MIR Image Registration from KOMPSAT-3A via Radiometric and Feature Contrast Enhancement

The mid-wave infrared (MIR) sensor onboard KOMPSAT-3A captures thermal imagery within the 3.3–5.2 μm spectral range, enabling detailed thermal analysis under both daytime and nighttime conditions. These images are extensively utilized in various applications, including urban heat island monitoring, drought assessment, environmental analysis, and thermal anomaly detection. However, temporal discrepancies between daytime and nighttime acquisitions frequently introduce radiometric inconsistencies and relative geometric dissimilarities, which pose significant challenges for accurate image...

Korean J. Remote Sens. 2025; 41(1): 31-40

Tracking Vegetation Recovery after the 2019–2020 Wildfires in Tumbarumba, Australia, Using a High-Resolution Image Fusion Dataset

Wildfires have grown in scale and intensity over recent decades, profoundly impacting global carbon cycles. For instance, the 2019–2020 Australian wildfire burned 18.6 million hectares of forest and released 715 million tonnes of carbon dioxide. Assessing vegetation recovery after wildfire at such scales is challenging due to conventional satellite products’ spatial and temporal resolution limitations. This study addresses these limitations by fusing two datasets, MODerate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 product and Landsat 8/9 Nadir Bidirectional Reflectance D...

Korean J. Remote Sens. 2025; 41(1): 41-51

Reconstruction of Missing Data in GOCI-II Chlorophyll-a Imagery Using Deep Learning-Based Spatio-Temporal Approaches

Geostationary Ocean Color Imager-II (GOCI-II), an ocean color satellite sensor that continuously observes the seas and coasts around the Korean Peninsula, is widely used to analyze and study not only short-term changes in the ocean but also mid- to long-term changes in the marine environment. However, optical satellite observation has limitations in that missing values exist due to weather conditions such as clouds. These missing values are the biggest obstacle to continuously monitoring changes in the marine environment and predicting future trends. In this study, a robust missing value resto...

Korean J. Remote Sens. 2025; 41(1): 53-64

A Study on GNSS Interference Signal Detection Using CYGNSS

This study proposes a method to detect Global Navigation Satellite System (GNSS) interference signals by analyzing the noise levels in the GNSS L1 frequency band observed by low Earth orbit satellites, utilizing data from the Cyclone GNSS (CYGNSS) Level 1 Science Data Record Version 3.2. Using Delay Doppler Map (DDM) noise floor data from the Pacific Ocean, where GNSS interference signals are theoretically absent, the baseline noise distribution was defined. This baseline was then compared against ddm_noise_floor data from regions such as the Middle East and areas near the Korean Peninsula, wh...

Korean J. Remote Sens. 2025; 41(1): 65-72

Early-Stage Cabbage Monitoring Using UAV Multispectral Imagery and a Modified Faster R-CNN: Enhanced Object Delineation with ExG-Based Dissolve

Early and accurate monitoring of crop growth is crucial for precision agriculture. This study developed and evaluated a novel framework for precision monitoring of early-stage cabbage (Brassica oleracea var. capitata) using Unmanned Aerial Vehicle (UAV) multispectral imagery and a modified Faster Region-based Convolutional Neural Network (Faster R-CNN). A DJI Matrice 300 RTK UAV equipped with RGB and RedEdge-MX multispectral sensors acquired high-resolution imagery of a cabbage testbed in Goesan-gun, South Korea. A Faster R-CNN model, incorporating a ResNet-50 backbone and Feature Pyramid Netw...

Korean J. Remote Sens. 2025; 41(1): 73-86

Analyzing the Impact of Interior Orientation Parameter Settings, the Number of GCPs, and GCP Positional Accuracy on Orthomosaic Quality

Unmanned Aerial Vehicle (UAV) orthomosaics are widely used in various fields, including construction, environmental monitoring, and real estate, and their quality is influenced by the accuracy of interior orientation. In this study, the modes were divided into “All mode” and “Part mode” based on the interior orientation parameter settings, and lens distortion was compared between the two modes. Additionally, the effects of the number of Ground Control Points (GCPs) and their positional accuracy on the positional accuracy of UAV-based orthomosaics were evaluated for each...

Korean J. Remote Sens. 2025; 41(1): 87-100

Estimation and Validation of Photosynthetically Available Radiation at the Sea Surface Using GOCI-II Data

Photosynthetically available radiation (PAR), used in primary productivity research, is calculated from the amount of solar radiation reaching the sea surface in the 400-700 nm wavelength band. This study developed and validated a Geostationary Ocean Color Imager II (GOCI-II)-based PAR algorithm to increase the utilization of GOCI-II and contribute to the production of accurate input data for primary productivity research in the waters around the Korean Peninsula. The algorithms used in MODIS and GOCI PAR studies were improved to suit the spatiotemporal and spectral resolution of GOCI-II, and ...

Korean J. Remote Sens. 2025; 41(1): 101-110

Generating High-Resolution Labels Using a Low-Resolution Amazon Deforestation Dataset and Pseudo-Labeling Techniques

The Amazon plays a crucial role in mitigating global warming and preserving biodiversity, which are vital for the Earth's environment. However, deforestation has been ongoing for an extended period. Particularly, the vast scale of the region poses challenges in accurately identifying deforested areas, highlighting the growing importance of leveraging satellite information to prevent further damage and develop effective restoration plans. This study proposes a deep learning-based method to automatically generate high-resolution deforestation labels using low-resolution satellite imagery and exi...

Korean J. Remote Sens. 2025; 41(1): 111-119

Developing a Geospatial Framework for Analyzing Industrial Heat Islands: A Hierarchical Approach to Characterize IHI and GWR-Based Factor Identification

An industrial heat island (IHI) refers to the phenomenon in which industrial complexes exhibit higher temperatures than their surrounding areas. It is classified as a subtype of the urban heat island (UHI). Most IHI studies have focused on the local scale (0.1–10 km), limiting their integration with broader UHI research. Additionally, the factors that influence UHI within industrial complexes remain understudied. To address these gaps, this study proposes a geospatial framework to analyze IHI, strengthen its connection with UHI research, and identify key industrial factors. The primary d...

Korean J. Remote Sens. 2025; 41(1): 121-141

Transformer-Based Deep Learning Models for ERS SAR Image Super-Resolution

Synthetic aperture radar (SAR) image restoration plays a crucial role in enhancing spatial resolution and suppressing noise, enabling various applications such as terrain analysis, disaster monitoring, and military reconnaissance. However, SAR images exhibit speckle noise, strong directional characteristics, and multiple scattering effects, making them challenging to process with conventional image restoration techniques. To address these challenges, convolutional neural network (CNN)-based models such as superresolution convolutional neural network (SRCNN), very deep super-resolution (VDSR), ...

Korean J. Remote Sens. 2025; 41(1): 143-152

Monitoring the Impact of Urban Development in Magok on Surface Urban Heat Island Intensity

The reduction of natural cover and the increase in artificial surface caused by urbanization lead to surface urban heat island. New towns are considered an appropriate study site to analyze surface urban heat island affected by land cover transformations, due to their development characteristics of rapid urbanization. This study aims to monitor surface urban heat island intensity (SUHII) resulting from land cover changes. We study Magok New Town as a representative urban development case in Seoul, South Korea. SUHII is defined as the difference in the mean land surface temperature (LST) betwee...

Korean J. Remote Sens. 2025; 41(1): 153-172

Detecting Inaccessible Flood Damage in North Korea Using Sentinel-1 Synthetic Aperture Radar Imagery: A Case of Flooding at 2023 Summer

Current global climate change increases water-related disasters like floods, making the Korean Peninsula vulnerable to typhoons and rainfall damage. In North Korea, flood analysis relies on remote sensing due to poor infrastructure and observation systems. Synthetic aperture radar (SAR) imagery, unaffected by clouds, is effective for flood analysis. This study used Sentinel-1 SAR image differencing to assess 2023 flooding in North Korea. After differencing images from North and South Korea, random forest classification identified flooded areas. The accuracy of this method was confirmed by its ...

Korean J. Remote Sens. 2025; 41(1): 173-184

Improvement of Classification Accuracy in Object-Based Classification Method Using Normalized Difference Water Index

The technology for classifying objects using remote sensing images has gained increasing attention recently with the advent of high-resolution satellite imagery and advancements in deep learning techniques. One of the representative methods for classifying objects is object-based image classification, which involves segmenting the image and using the attributes of the segmented regions to generate classification results at the object level. In urban areas, the presence of various buildings with different colors and shapes, along with numerous shadows, makes it difficult to distinguish objects ...

Korean J. Remote Sens. 2025; 41(1): 185-197

Prioritization Reservoir Dredging Sites Using Sentinel-1 SAR Imagery

The increasing frequency of extreme weather events caused by climate change has highlighted the importance of systematic reservoir management to ensure a stable supply of agricultural water. Dredging, in particular, is one of the key management strategies to prevent reservoir functionality degradation due to sediment accumulation. However, dredging projects are often carried out relying heavily on the subjective judgment and experience of field personnel due to constraints in time and resources. This study proposes a method for objectively prioritizing dredging sites using Sentinel-1 Synthetic...

Korean J. Remote Sens. 2025; 41(1): 199-208

High-Accuracy Tree Type Classification in Urban Forests Using Drone-Based RGB Imagery and Optimized SVM

Accurate and efficient tree type classification in urban forests is crucial for effective management, informed policy decisions, and enhancing urban resilience, particularly with increasing urbanization and climate change. This study developed and evaluated a practical methodology for classifying coniferous and broadleaf trees in the Chungbuk National University Arboretum, South Korea. The study utilized drone-acquired, high-resolution RGB imagery and a Support Vector Machine (SVM) classifier. The workflow encompassed drone image acquisition, concurrent ground truth data collection, image prep...

Korean J. Remote Sens. 2025; 41(1): 209-223

A Case Study of Tropical Night Detection Using Vehicle Observation-Based Temperature Sensor

In this study, we mainly used a special observation dataset from vehicle observation-based temperature sensors for a tropical night episode that occurred on August 4-5, 2022. We also use a local data assimilation and prediction system (LDAPS) to understand the influence of determinant meteorological conditions on tropical night intensity (TNI), particularly at the local scale level in Gangneung City. For the purpose of comparing characteristics of anthropogenic heat and TNI, we divided the study area into two road sections (urban and suburban). Interestingly, results demonstrated that strong (...

Korean J. Remote Sens. 2025; 41(1): 225-239

Erratum to: Satellite Image-Based Field Compost Detection Using Deep Learning

Korean Journal of Remote Sensing 2024, 40(6), 1409-1419. DOI: https://doi.org/10.7780/kjrs.2024.40.6.3.3대한원격탐사학회지 제40권 6호에 게재된 상기 논문에서 본문 일부를 다음과 같이 정정합니다.

Korean J. Remote Sens. 2025; 41(1): 241-242

Current Issue Vol. 41, No. 1, February 2025

Review
4D NARAE-Weather Data Platform Services for Supporting TBO

Jiyeon Kim, Sang-il Kim, Do-Seob Ahn, Hoon Choi

Korean Journal of Remote Sensing 2025; 41(1): 1-10

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Review
Hydrological and Mathematical Framework of Ensemble Learning Regression for Estimating Discharge in Ungauged River Basins

Donghwan Kim

Korean Journal of Remote Sensing 2025; 41(1): 11-29

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Research Article
Daytime and Nighttime MIR Image Registration from KOMPSAT-3A via Radiometric and Feature Contrast Enhancement

Nayoung Kim, Kwangjae Lee, Yeseul Kim, Taeheon Kim, Youkyung Han

Korean Journal of Remote Sensing 2025; 41(1): 31-40

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Research Article
Tracking Vegetation Recovery after the 2019–2020 Wildfires in Tumbarumba, Australia, Using a High-Resolution Image Fusion Dataset

Beomjun Kang, Sungchan Jeong, Seokjin Han, Juwon Kong

Korean Journal of Remote Sensing 2025; 41(1): 41-51

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Online First Articles

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1589

National Disaster Management and Monitoring Using Satellite Remote Sensing and Geo-Information

Jongsoo Park, Hagyu Jeong, Junwoo Lee
Review Korean Journal of Remote Sensing 2024; 40(5): 813-832
1454

Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

Md Asrakul Haque, Md Nasim Reza, Mohammod Ali, Md Rejaul Karim, Shahriar Ahmed, Kyung-Do Lee, Young Ho Khang, Sun-Ok Chung
Review Korean Journal of Remote Sensing 2024; 40(4): 319-341
733

Application of Geo-Segment Anything Model (SAM) Scheme to Water Body Segmentation: An Experiment Study Using CAS500-1 Images

Hayoung Lee, Kwangseob Kim, Kiwon Lee
Research Article Korean Journal of Remote Sensing 2024; 40(4): 343-350
714

Performance Comparison of Water Body Detection from Sentinel-1 SAR and Sentinel-2 Optical Imagery Using Attention U-Net Model

Il-Hoon Choi, Eu-Ru Lee, Hyung-Sup Jung
Research Article Korean Journal of Remote Sensing 2024; 40(5): 507-523

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Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review

Md Asrakul Haque, Md Nasim Reza, Mohammod Ali, Md Rejaul Karim, Shahriar Ahmed, Kyung-Do Lee, Young Ho Khang, Sun-Ok Chung
Review Korean Journal of Remote Sensing 2024; 40(4): 319-341
134

Comparison of Deep Learning-Based SAR-to-Optical Image Translation Models for High Spatial Resolution Optical Image Restoration

Soyeon Park, Geun-Ho Kwak, Eui Ho Hwang, No-Wook Park
Research Article Korean Journal of Remote Sensing 2024; 40(6): 881-893
127

Application of Geo-Segment Anything Model (SAM) Scheme to Water Body Segmentation: An Experiment Study Using CAS500-1 Images

Hayoung Lee, Kwangseob Kim, Kiwon Lee
Research Article Korean Journal of Remote Sensing 2024; 40(4): 343-350
120

Assessing the Geometric Integrity of Cylindrical Storage Tanks: A Comparative Study Using Static Terrestrial Laser Scanning and Total Station

Mansour Alghamdi, Jinha Jung
Research Article Korean Journal of Remote Sensing 2024; 40(3): 243-255

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Announcement

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Notification of the Publication of KJRS (Vol. 41, No. 1, Feb. 2025)

Dear Members of the Korean Society of Remote Sensing,

We are pleased to announce the publication of the February 2025 issue (Vol. 41, No. 1) of the Korean Jo...

March 6, 2025
Call for Papers for the April 2025 Issue (Submission Due Date: Mar. 31)

March 4, 2025
Call for Papers - Special Issue on Advances in Geospatial Technologies (Submission Due Date: Mar. 31...

February 25, 2025
Call for Papers for the February 2025 Issue (Submission Closed)

January 8, 2025

About the Journal

Aims and Scope

Korean Journal of Remote Sensing serves the remote sensing community with the publication of results based on theory, technology and applications of remote sensing fields. Thoroughly interdisciplinary, this journal focuses on the research and development of remote sensing science and technologies and promotes the exchange of expert knowledge among remote sensing researchers as well.

Areas of interest include, but are not necessarily restricted to:
– Remote Sensor Technology
– Novel Sensor Design and Platforms Development ...

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