GEO DATA

Data Articles

Analysis of Slow-moving Landslide-Prone Areas in Gangwon State Using C-band and L-band InSAR: Ho-Yeong You, Taeseok Lim, Boram Lee, Yoon-Kyung Lee, Jung-Hyun Lee, Hyuck-Jin Park, Sang-Wan Kim; Received March 31, 2026 Accepted April 9, 2026 Published online April 13, 2026; DOI: https://doi.org/10.22761/GD.2026.0011 [Epub ahead of print]

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Abstract PDF: This study constructed slope displacement data for the Gangwon State by applying persistent scatterer (PS) and distributed scatterer (DS) synthetic aperture radar interferometry (InSAR) time-series analysis to Sentinel-1 C-band and ALOS-2 PALSAR-2 L-band data, and evaluated the applicability of each sensor for slow-moving landslide detection in mountainous terrain dominated by dense forest cover. Following co-registration and topographic phase removal, sequential phase linking was applied to optimize interferometric phases, and line-of-sight (LOS) displacement velocity maps were generated through time-series analysis integrating both PS and DS pixels. The results revealed that Sentinel-1 observations were largely confined to exposed surfaces such as urban areas and road cuts, whereas ALOS-2 PALSAR-2 provided spatially continuous coverage extending into forested slopes. On slopes steeper than 10°, the valid pixel ratios for ALOS-2 PALSAR-2 reached 78.0%, 68.0%, and 57.0% for the 10-20°, 20-30°, and ≥30° slope ranges, respectively, compared to only 11.4%, 5.6%, and 4.7% for Sentinel-1. In the land-cover-based comparison, Sentinel-1 achieved moderate valid pixel ratios for cropland (49.3%) and grassland (37.0%) through phase optimization, but only 6.5% for forested areas, whereas ALOS-2 PALSAR-2 maintained 66.0% even in forests. In the aspect-wise comparison, ALOS-2 PALSAR-2 sustained observation densities exceeding 30% across all slope orientations, including both satellite-facing and satellite-away slopes. Furthermore, time-series analysis of two slow-moving landslide-prone sites identified from the ALOS-2 PALSAR-2 velocity map revealed mean LOS velocities of -28.2, +12.9, -39.7 mm/yr for three slope sectors, all exhibiting linear displacement trends over approximately 5 years. These results demonstrate that L-band InSAR displacement data can serve as a fundamental dataset for regional-scale early detection and long-term monitoring of slow-moving landslides in the mountainous terrain of Gangwon.

Waterbody Detection and Reservoir Water Level Prediction Using Bayesian Mixture Models with Sentinel-1 GRD Data: DongHyeon Yoon, Ha-Eun Yu, Euiho Hwang, Ki-mook Kang, Gibeom Nam, Jin-Gyeom Kim; GEO DATA. 2025;7(1):18-26. Published online February 5, 2025; DOI: https://doi.org/10.22761/GD.2024.0052

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Abstract PDF: In this study, we used a Bayesian mixture model (BMM) to monitor water surface areas and estimate water levels in Yeongcheon Dam through Sentinel-1 synthetic aperture radar (SAR) imagery. Reservoirs serve vital functions such as flood control, drought mitigation, and ecosystem support, highlighting the importance of precise monitoring of their water surface and level variations, especially in the context of climate change and increased human impact. The BMM method was employed to accurately delineate water boundaries, benefiting from SAR’s capability to capture data regardless of weather conditions. Regression analysis was conducted between the extracted water surface area and observed water levels to create a predictive model, yielding a highly accurate equation with an R² core of 0.981 on the test set. This result indicates a strong correlation between water surface area and water level, affirming the model’s reliability in estimating water levels based solely on surface area data. One of the key findings of this study is that even with a 10 m spatial resolution, reliable water level inferences can be made using water surface area as a proxy. The mean absolute error values obtained validate the model’s capability to monitor water level fluctuations with a satisfactory degree of accuracy. Despite limitations in detecting narrow tributaries or other small-scale features due to SAR resolution, the model performs well overall in monitoring broad water bodies. These findings underscore the potential of Sentinel-1 SAR data for effective reservoir monitoring, especially where real-time water level data may be lacking. For future research, higher-resolution data or complementary algorithms may further enhance detection accuracy for smaller and more complex water features, contributing to more refined water resource management strategies.

Original Papers

Detection of Floating Debris in the Lake Using Statistical Properties of Synthetic Aperture Radar Pulses: Donghyeon Yoon, Ha-eun Yu, Moung-Jin Lee; GEO DATA. 2023;5(3):185-194. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0032

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This study developed the European Space Agency (ESA) Setinel-1 Ground Range Detected (GRD) time series analysis model for monitoring floating debris in lake areas through Google Earth Engine Application Programming Interface. The study aims to monitor floating debris caused by heavy rainfall efficiently. Regarding water resources and water quality management, floating debris from multipurpose dams requires continuous monitoring from the initial generation stage. In the study, a Synthetic Aperture Radar (SAR) time series analysis model that is easy to identify water bodies was developed due to low accessibility in large areas. Although SAR satellite images could be used to observe inland water environments, debris detection on water surface surfaces has yet to be studied. For the first time, this study detected floating debris patches in a wide range of lakes from GRD imagery acquired by ESA’s Sentinel-1 satellite. It demonstrated the potential to distinguish them from naturally occurring materials such as invasive floating plants. In this study, the case of Daecheong Dam, in which predicted floating debris was detected after heavy rain using Sentinel-1 GRD data, is presented. It could quickly detect various floating debris flowing into dams used as a source of drinking water and serve as a reference for establishing a collection plan.

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Quantitative Detection of Floating Debris in Inland Reservoirs Using Sentinel-1 SAR Imagery: A Case Study of Daecheong Reservoir
Sunmin Lee, Bongseok Jeong, Donghyeon Yoon, Jinhee Lee, Jeongho Lee, Joonghyeok Heo, Moung-Jin Lee
Water.2025; 17(13): 1941. CrossRef

Research on Building AI Learning Dataset for Synthetic Aperture Radar Waterbody Detection through Optical Satellite Image Fusion: Joonhyuk Choi, Ki-mook Kang, Euiho Hwang; GEO DATA. 2023;5(3):177-184. Published online September 27, 2023; DOI: https://doi.org/10.22761/GD.2023.0029

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For the spatiotemporal analysis of water resources and disasters, water body detection using satellite imagery is crucial. Recently, AI-based methods have been widely employed in water body detection using satellite imagery. To use these AI techniques, a substantial amount of training data is required. When creating training data for water body detection, optical imagery and synthetic aperture radar (SAR) imagery have their respective strengths and weaknesses. To use the advantages of both, this study proposes a water body detection method through the fusion of optical and SAR imagery. The results of the proposed model show an Intersection over Union of 0.612 and an F1 score of 0.759, which is better compared to using either optical or SAR imagery alone. This research presents a method that can easily generate a large amount of water body data, making it promising for use as AI training data for water body detection.

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A Comprehensive Review of Remote Sensing for Water-Related Disaster Management in South Korea: Focus on Floods and Droughts
Eui-Ho Hwang, Jin-Gyeom Kim, Jang-Yong Sung, Ki-Mook Kang
Korean Journal of Remote Sensing.2024; 40(5-2): 833. CrossRef

A Study on C-band Synthetic Aperture Radar Soil Moisture Estimation Based on Machine Learning Using Soil Physics, Topography, and Hydrological Information: Jeehun Chung, Yonggwan Lee, Jinuk Kim, Wonjin Jang, Seongjoon Kim; GEO DATA. 2023;5(3):137-146. Published online September 22, 2023; DOI: https://doi.org/10.22761/GD.2023.0026

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Abstract PDF: In this study, we applied machine learning to estimate soil moisture levels in South Korea by harnessing data from the Sentinel-1 C-band synthetic aperture radar (SAR). Our approach incorporated not only the relationship between backscattering coefficients and soil moisture but also diverse physical characteristics. This encompassed topographic information, soil physics data, and antecedent precipitation which is a hydrological factor influencing the initial condition of soil moisture. We applied a variety of machine-learning techniques and conducted a comprehensive analysis to compare the performance of each model.

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