This study investigates spatiotemporal variations in column-average dry-air mole fractions of methane (XCH4) over the Korean Peninsula from 2019 to 2024 using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. To assess data accuracy, satellite retrievals were compared with ground-based Total Carbon Column Observing Network (TCCON) measurements at Anmyeondo, Korea, and Saga, Japan. Despite limited data availability at Anmyeondo (N = 7) due to harsh coastal conditions, the validation showed a high correlation (R > 0.88) and consistent seasonal variations at both sites, confirming the accuracy of TROPOMI retrievals. The results revealed a distinct "west-high, east-low" spatial pattern of XCH4 over the Korean Peninsula. This distribution reflects the concentration of major anthropogenic sources in western regions, including the densely populated Seoul Metropolitan Area, large-scale industrial complexes, and coal-fired power plants, showing strong qualitative and quantitative agreement with the Emissions Database for Global Atmospheric Research (EDGAR) 2025 emission inventory. In general, the mean XCH4 concentration increased continuously across the Korean Peninsula, except for a temporary stagnation in 2022. Notably, a synchronized rapid surge was observed across all major regions in 2024, peaking at 1912.58 ppb. Regional analysis indicated that, while local industrial emissions (e.g., in Gyeongnam) drove regional heterogeneity until 2023, the sharp increase in 2024 was likely driven by strong external factors, such as the global rise in background methane levels or large-scale meteorological influences. These findings provide important insights into regional variations in methane levels and emissions across the Korean Peninsula. These results demonstrate that TROPOMI not only effectively captures anthropogenic emission patterns aligned with EDGAR but also identifies potential natural sources unique to the Korean Peninsula's environmental characteristics. By bridging the gap between existing emission inventories and real-world observations, our findings highlight the critical role of satellite-based monitoring in establishing comprehensive, data-driven strategies for regional methane management and achieving national climate goals.
This study developed a landslide susceptibility map (LSM) for the Angren area of Uzbekistan by comparing a frequency ratio (FR) model with a convolutional neural network optimized using the grey wolf optimizer (CNN-GWO). The study area features steep mountain slopes characterized by Neogene sedimentary lithology, overlain by Quaternary loess deposits, and is affected by intensive mining activities, making it highly susceptible to seasonal landslides. The susceptibility mapping was performed by integrating landslide inventory with comprehensive topographic parameters, soil characteristics, land-use data, normalized difference vegetation index (NDVI), and geological information. The FR model assessed the statistical correlation between landslide occurrences and individual influencing factors, whereas the CNN-GWO model integrated all raster layers to identify complex spatial patterns in landslide susceptibility. The results show that the susceptibility maps generated by the FR and CNN-GWO models display comparable spatial patterns, with area under the curve values of approximately 78.11% and 80.11%, respectively. These findings provide a useful framework for spatial planning, infrastructure protection, and landslide risk reduction in the mountainous Angren area.
Urbanization increases impervious surfaces and intensifies the surface urban heat island (SUHI). Satellite-based land surface temperature (LST) is widely used to assess SUHI, but limited high-resolution nighttime observations have led many studies to focus on daytime conditions. In this study, using 70-m ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST, we quantify seasonal (summer vs. winter) and diurnal (day vs. night) variability in surface urban heat island intensity (SUHII) in Suwon, South Korea, based on ten clear-sky acquisitions (2022-2025). A two-way repeated measures ANOVA confirms that SUHII is significantly stronger and more spatially heterogeneous in summer than in winter, and during the day than at night. To explain these time-dependent SUHII patterns in relation to urban form while minimizing scale-mismatch information loss, we aggregate 30-m local climate zone (LCZ) compositions within each 70-m SUHII pixel and estimate time-specific effects via compositional regression. Natural LCZs are generally associated with cooling, with tree-related classes showing cooling that varies with canopy density. Water (LCZ G) exhibits a pronounced day-night contrast, shifting from daytime cooling to nighttime warming. Built-up LCZs increase SUHII, with high-density development (LCZ 1-3) consistently producing warming across all times. Vertical structure effects are time-dependent, with stronger warming in low-rise areas during the day and in high-rise areas at night. This suggests that shading and solar exposure dominate daytime surface heating in low-rise buildings, whereas nocturnal warming in high-rise areas is more strongly shaped by radiative trapping and heat storage within urban canyons. These results quantify time-dependent patterns in SUHII and LCZ-specific heat responses, supporting a basis for identifying season-and time-specific heat-vulnerable areas and tailoring mitigation by urban form.
This study investigates the variation characteristics of the mass extinction efficiency (MEE) of particulate matter (PM; PM10, PM2.5-10, and PM2.5) in the Busan port (Busan, South Korea) area using long-term Scanning Light Detection and Ranging (LiDAR) and in situ measurement data. The results show that the mean MEE of PM2.5 (7.72 +/- 3.86 m & sup2; g(-)& sup1;) is significantly higher than that of PM10 (5.19 +/- 1.99 m & sup2; g(-)& sup1;) and PM2.5-10 (1.95 +/- 1.26 m & sup2; g(-)& sup1;), confirming the dominant optical role of fine particles. MEE exhibits a decreasing trend with increasing PM(2.5)mass concentration due to particle growth and aggregation under high-loading conditions, while it increases with a higher PM2.5/PM10 ratio, indicating the importance of particle composition. The combined analysis of MEE and relative humidity (RH) reveals a nonlinear interaction: MEE increases under moderate RH (60-70%) due to hygroscopic growth but decreases at high concentration or low RH (< 40%) as particle agglomeration dominates. These results demonstrate that MEE is controlled by the coupled effects of concentration, composition, and humidity rather than by a single variable. The findings provide a physical basis for developing region-specific correction factors for converting LiDAR extinction coefficients to PM mass concentrations in coastal and port environments.
Unmanned aerial vehicles (UAVs) enable micro-scale thermal mapping in urban environments, yet thermal imagery records brightness temperature (BT) that is biased by land-surface emissivity (epsilon) and atmospheric effects. This study develops a single-channel (SC) land surface temperature (LST) retrieval framework that integrates UAV-based thermal and multispectral imagery (MicaSense Altum-PT) with in-situ observations. Pixel-wise epsilon was estimated using the empirical normalized difference vegetation index (NDVI)-epsilon relationship developed for the ASTER spectral response function (SRF), applied to NDVI derived from the Altum-PT RED (668 nm) and near-infrared (842 nm) bands. Background temperature (Tbkg), corresponding to the reflected apparent temperature, was measured using a crumpled aluminum foil temporarily placed within the imaging footprint, which served as a near-perfect reflector to capture ambient longwave radiation, while air temperature (Ta) from on-site loggers; atmospheric transmittance (tau) is fixed under clear-sky conditions. The workflow is demonstrated over the Namdong Industrial Complex (Incheon, Republic of Korea) at 11:00, 13:00, and 15:00 (UTC+9). We quantify the correction magnitude as AT=BT-LST within a LiDAR-derived walkable-surface mask and further stratify the results by land cover (Natural vs. Artificial pavements). AT is predominantly negative (median: -1.85, -0.95, -0.73 K), indicating systematic BT underestimation relative to LST. Artificial pavements exhibit larger negative AT and greater variability than natural cover, reflecting material-dependent emissivity and surface-condition heterogeneity. The magnitude of |AT|generally decreases from morning to afternoon. The results support the use of SC-retrieved LST rather than raw BT for threshold-based assessments (e.g., heat-risk mapping) in pedestrian contexts. Although validated on a single date and site without absolute blackbody references, the framework shows consistent behavior across times and land-cover types, highlighting its utility for urban micro-climate applications.
Advances in Earth observation technologies have enabled mid-sized satellites to achieve high radiometric performance at relatively low cost, expanding their use to quantitative remote sensing applications. Compact Advanced Satellite 500-4 (CAS500-4), equipped with visible-to-near-infrared sensors including a red-edge band and designed to provide nearly 5-meter multispectral imagery every 3 days, requires a reliable radiometric monitoring framework. Cross-sensor comparison offers an efficient means of validating radiometric stability, particularly through reference sensors such as LANDSAT-8 Operational Land Imager (OLI), whose long-term calibration record ensures high radiometric consistency. This study evaluates whether forest-dominant regions on the Korean Peninsula, which represent the primary observation target of CAS500-4, can serve as suitable ground targets for cross-validation between LANDSAT-8 radiance and KOMPSAT-3 multispectral digital number (DN) values. Five KOMPSAT-3 scenes acquired near-nadir on 13 January 2025 were paired with the same-day Landsat-8 imagery. To mitigate geometric and spatial-resolution mismatches, zonal statistics and a pixel-based sliding-window sampling method (1,681 windows) were applied. Results showed that zonal-mean DN values from KOMPSAT-3 were highly stable across sliding windows, with standard deviations generally below 2 DN. Derived radiance conversion coefficients in the visible bands differed by less than 6% from previous KOMPSAT-3 radiometric studies, demonstrating the strong feasibility of the proposed method. Larger discrepancies in the near-infrared band were attributed to water-dominant landcover and nonlinear sensor response at low DN levels. Overall, forest regions exhibited stable reflectance characteristics suitable for radiometric comparison, suggesting that Korean forests provide a practical and effective foundation for CAS500-4 radiometric validation after launch.
Estimating photosynthetically active radiation (PAR) is crucial for quantifying ecosystem productivity through photosynthesis. In particular, providing continuous estimates of direct PAR (PARdir) and diffuse PAR (PARdif) is important for assessing ecosystem photosynthesis. The Breathing Earth System Simulator (BESS) has proven effective in estimating global PARdir and PARdif using an artificial neural network (ANN) that incorporates continuous satellite-based atmosphere products and elevation. However, BESS PAR does not account for topographic correction due to its relatively coarse resolution. The Perez-Driesse model is commonly used for topographic correction by partitioning incoming PAR into five components: PARdir, anisotropic PARdif, isotropic PARdif, horizontal brightness PARdif, and ground reflected PARdif. Among these, isotropic PARdif and ground reflected PARdif are strongly influenced by the surrounding visible sky and terrain fraction. However, the Perez-Driesse model oversimplifies the sky view factor (SVF) and ground-reflected PARdif calculations by considering only the slope at a given point. To address this issue, we leveraged horizon angles from a digital elevation model (DEM) by scanning azimuth directions from 0 degrees to 360 degrees at 3 degrees intervals to more accurately estimate the SVF for isotropic PARdif, and we accounted for adjacent terrain slopes and distances for ground-reflected PARdif. We applied this improved SVF Perez-Driesse model to BESS PAR estimates, which combine 2 km-resolution atmospheric products from the GEO-KOMPSAT-2A satellite with the 30 m-resolution Copernicus DEM. Validation with LI-190R quantum sensor data from rooftop observations demonstrated significant improvements after applying topographic correction. For southward orientations, the coefficient of determination (R2) improved from 0.52 to 0.78, relative bias (rbias) from 28.17% to-0.78%, mean absolute error (MAE) from 429.3 to 299.4, and root mean squared error (RMSE) from 553.3 to 378.7. For westward orientations, R2 increased from-0.36 to 0.55, rbias from-44.63% to 23.80%, MAE from 477.3 to 243.7, and RMSE from 566.3 to 340.0. Analysis of two study sites with different slopes on the Korean Peninsula in 2021 showed that seasonal variations in shadow ratios reduced PARdir by 40-80% under low solar positions. The rbias of PARdif was influenced by increased cloud cover and aerosols during summer. The effect of terrain aspect was pronounced in instantaneous topographic corrections, particularly due to differences between aspect and solar azimuth angle, whereas at the annual scale, north-south aspect differences were more evident. Our results showed that Perez-Driesse-based topographically corrected PAR can vary by up to 40% in rugged terrain, whereas SVF Perez-Driesse-based PAR varies by nearly 20%, leading the significant variation in photosynthesis estimation.
