Unmanned Aerial Vehicle (UAV) high-resolution remote sensing imagery has been used for unprecedented coastal environment monitoring with ground sampling distance and time intervals of a few centimeters and seconds, respectively. However, high spatial-time resolutions of UAV remote sensing data consist of unexpected signals from water surface level changes induced by wind-driven currents and waves. This leads to non-linear and non-stationary forms of sun and sky glints in the UAV sea surface image. Consequently, these surface glints interfere with the detection of water body reflections and objects, reducing the accuracy and usability of the measurements. This study employed Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) to separate the spatial periodicity of time-continuous multispectral images of the sea surface from the original data and retain non-oscillatory signals called residual images. The residual images effectively represented the spatial-temporal radiance and flow variations in the water body by correcting the regions of surface glint. This study presents three key findings: First, homogeneous surface radiance data with surface glint removed from the raw image sequence was acquired using FA-MEMD. Second, the continuous surface glint removal effect is validated through water-leaving radiance (L_w-SBA) measurements obtained via the Skylight-Blocked Approach (SBA) method. Comparisons showed that R² values for the data obtained from clear water before and after surface glint removal were 0.02 and 0.56 with RMSE values of 8.37 × 10⁻⁵ and 5.51 × 10⁻⁵ W·m⁻²·sr⁻¹, respectively, indicating an improvement rate of 34.19%. Third, a comparative analysis with previous study methods demonstrated that our approach yielded spatially and temporally uniform homogeneous surface radiance data with less variability than traditional methods. The spatially and temporally synchronized residual images and the L_w-SBA data showed high similarity, confirming that the FA-MEMD technique effectively removed the surface glint from wave-induced roughness, enhancing the reliability of high-resolution UAV sea color observations.

Increasing global plastic usage has raised critical concerns regarding marine pollution. This study addresses the pressing issue of floating marine macro-litter (FMML) by developing a novel monitoring system using a multi-spectral sensor and drones along the southern coast of South Korea. Subsequently, a convolutional neural network (CNN) model was utilized to classify four distinct marine litter materials: film, fiber, fragment, and foam. Automatic atmospheric correction with the drone data atmospheric correction (DROACOR) method, which is specifically designed for currently available drone-based sensors, ensured consistent reflectance across altitudes in the FMML dataset. The CNN models exhibited promising performance, with precision, recall, and F1 score values of 0.9, 0.88, and 0.89, respectively. Furthermore, gradient-weighted class activation mapping (Grad-CAM), an object recognition technique, allowed us to interpret the classification performance. Overall, this study will shed light on successful FMML identification using multi-spectral observations for broader applications in diverse marine environments.

The consecutive submesoscale sea surface processes observed by an unmanned aerial vehicle (UAV) were used to decompose into spatial waves and current features. For the image decomposition, the Fast and Adaptive Multidimensional Empirical Mode Decomposition (FA-MEMD) method was employed to disintegrate multicomponent signals identified in sea surface optical images into modulated signals characterized by their amplitudes and frequencies. These signals, referred to as Bidimensional Intrinsic Mode Functions (BIMFs), represent the inherent two-dimensional oscillatory patterns within sea surface optical data. The BIMFs, separated into seven modes and a residual component, were subsequently reconstructed based on the physical frequencies. A two-dimensional Fast Fourier Transform (2D FFT) for each high-frequency mode was used for surface wave analysis to illustrate the wave characteristics. Wavenumbers (K_x, K_y) ranging between 0.01–0.1 radm⁻¹ and wave directions predominantly in the northeastward direction were identified from the spectral peak ranges. The Optical Flow (OF) algorithm was applied to the remaining consecutive low-frequency modes as the current signal under 0.1 Hz for surface current analysis and to estimate a current field with a 1 m spatial resolution. The accuracy of currents in the overall region was validated with in situ drifter measurements, showing an R-squared (R²) value of 0.80 and an average root-mean-square error (RMSE) of 0.03 ms⁻¹. This study proposes a novel framework for analyzing individual sea surface dynamical processes acquired from high-resolution UAV imagery using a multidimensional signal decomposition method specialized in nonlinear and nonstationary data analysis.

The Ieodo plume is a distinctive suspended sediment plume near the Ieodo Ocean Research Station (I-ORS), located in the middle of the northern East China Sea. Because the Ieodo plume exhibits multiple different spatial scales, this study conducted an integrated remote sensing observation using satellites and unmanned aerial vehicles (UAVs) to observe its development and dispersion. Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) data were used to determine the plume’s spatial characteristics, broad-scale behavior, hourly variability, and turbidity characteristics. Also, TPXO model outputs were employed to evaluate the relationship between plume occurrence and tides, together with satellite imagery. Plume was repeatedly observed near the top of the Ieodo Seamount, with an affected extent of 11.4 ± 3.2 km in the east–west direction and 14.3 ± 4.1 km in the north–south direction. Moreover, hourly variations observed using GOCI-II showed that the Ieodo plume rotated clockwise with shifting tidal currents, forming a counterclockwise curved band or a ring-shaped structure. Total suspended solids (TSSs) in the plume reached their maximum when the southward component of the TPXO tidal current was dominant. Based on UAV optical surveys at the I-ORS, fine-scale morphology at the early stage of plume development was revealed, and it was confirmed that the Ieodo plume can occur even when it is not detected by satellite imagery. Furthermore, the u- and v-velocity vectors of the propagating Ieodo plume were derived by applying large-scale particle image velocimetry (LSPIV) to geometrically corrected sequential UAV imagery obtained in I-ORS. Plume speed was greatest near the source during the initial stage (0.81 ± 0.30 m s⁻¹) and gradually decreased to 0.34 ± 0.29 m s⁻¹ over distance. Based on the results above, we propose that the Ieodo plume is primarily generated by a pressure reduction associated with tidally accelerated currents over topography, driven by the Bernoulli effect. This study shows that an integrated satellite and UAV observation framework can effectively monitor rapidly evolving suspended sediment plumes. It can further help improve our understanding of dynamically driven submesoscale marine events.

The southern coast of Korea is characterized by a complex Rias coast and a barotropic flow regime dominated by strong tides. Under the influence of tidal forcing, complex current patterns develop regularly, leading to the generation of coastal eddies with spatial scales ranging from 0.1 to 10 km. These submesoscale eddies serve as an intermediary between mesoscale dynamics and small-scale turbulence, playing an important role in energy transfer. The goal of this study is to understand the dynamics of eddies observed during a field campaign near the Yokji region and Nodae Island using an eddy-resolving numerical model (with a grid resolution of 30 m). A numerical hydrodynamic model for the region was developed by the Deflt3D model and validated using Acoustic Wave and Current profiler data, velocity fields estimated from unmanned aerial vehicle imagery, and Sentinel-2 true-color imagery. To characterize eddy generation and interaction processes, the barotropic vorticity diagnostics for the depth-integrated flow are used. The local dynamics of submesoscale eddies highlight that the nonlinear advection is the leading local source of vorticity over the study area, followed by secondary contributions from bottom pressure torque and bottom drag curls interacting with topography. We employed a coarse-graining approach to estimate multiscale energy fluxes and kinetic energy transfer. The analysis suggests a tendency for localized upscale energy transfer into adjacent larger-scale background current during the eddy dissipation phase, and that barotropic instability near the cape is a potential contributor to the observed eddy generation. This framework will offer broader applicability for understanding submesoscale energetics and instability processes in tidally dominated shallow coastal systems.