Impact of Submesoscale Eddies on the Transport of Suspended Matter in the Coastal Zone of Crimea Based on Drone, Satellite, and In Situ Measurement Data

Kubryakov, A. A.; Лишаев, П. Н.; Чепыженко, А. И.; Aleskerova, A. A.; Kubryakova, Elena; Medvedeva, A. V.; Stanichny, S. V.

doi:10.1134/s0001437021020107

Cited by 10 publications

(11 citation statements)

References 34 publications

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“…Although there are new satellites with ocean color capabilities at higher spatial resolutions that can be used in these areas [84], these alternatives are still very few. In this work, the images from the multipurpose Landsat and Sentinel-2 satellite images, with a medium spatial resolution (30 and 10 m respectively), have been a good alternative, allowing, despite having less and wider bands, detailed studies of coastal processes at the sub-mesoscale level [4,23,25,85].…”

Section: Methodological Approachesmentioning

confidence: 99%

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

et al. 2023

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Coastal oceanographic processes, like up- and downwelling, topographic fronts, etc., have consequences in biological communities. In some cases, these processes show up as surface structures observable in satellite imagery at different scales. In this work, we focus on the persistent structures observed in the coastal marine protected area of Parque Interjurisdiccional Marino-Costero Patagonia Austral (PIMCPA), one of the most relevant areas of the Argentine coast in terms of biodiversity and productivity. Using 80 Landsat-8 30 m-resolution images from the years 2017–2021, more than 20 structures were identified in the PIMCPA that appear consistently across seasons in approximately the same areas. We focus on four of them, those that are persistent and of medium scale, whose dimensions do not extend in most cases more than 10 km from their region of generation, and describe their location and shape in detail, and analyze their dependence on forcing variables such as tides, wind, bathymetry, and seasonality. Tidal currents prove to be the most significant variable in the formation and evolution of the structures described, above wind or seasonal stratification, which play only a secondary role.

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Section: Methodological Approachesmentioning

confidence: 99%

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

et al. 2023

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“…In particular, high-spatiotemporal resolution image data from UAVs are the key factors in facilitating the observation and visualization of short-term submesoscale structures measuring hundreds of meters in surface area, which is challenging using other in situ instruments such as single point buoys, high-frequency radars, acoustic Doppler current profilers, and satellite sensors. These image data allow us to obtain intuitive interpretation and information regarding spatial changes over time, such as submesoscale current estimations from UAVs using wave dispersion analysis [14], wave-averaged current quantifications derived from optical data in the surf zone [15], wavelet-based surface current estimations from shore-based and UAV videos [16], surf zone circulation derivations by filtering the wave from optical video samples [17], submesoscale sea surface temperature variability analysis from UAVs and satellite measurements [18], surface wave property retrieval [19], and submesoscale eddy dynamic analysis on the transport of suspended matter [20].…”

Section: Introductionmentioning

confidence: 99%

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Kim,

Lee,

Jeong

et al. 2024

Remote Sensing

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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 (Kx, Ky) 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.

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“…However, for some areas, in a certain season, the manifestations of eddies in thermal contrasts may be weak or entirely absent [23]. In cases when satellite IR sensing fails to identify eddies due to small contrasts in the sea surface temperature fields, data in the visible range can be used, where the contrast can be significant due to different concentrations of chlorophyll-a (chl-a) [23][24][25], suspended solids (SSs) [26], colored dissolved organic matter (CDOM) [27], and even due to the distribution of broken ice [28]. These characteristics from satellite ocean color data are calculated from measurements of remotely sensed reflectance (Rrs) and can be attributed to optical characteristics of seawater.…”

Section: Introductionmentioning

confidence: 99%

Variations and Depth of Formation of Submesoscale Eddy Structures in Satellite Ocean Color Data in the Southwestern Region of the Peter the Great Bay

Lipinskaya,

Salyuk,

Golik

2023

Remote Sensing

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The aim of this study was to develop methods for determining the most significant contrasts in satellite ocean color data arising in the presence of a submesoscale eddy structure, as well as to determine the corresponding depths of the upper layer of the sea where these contrasts are formed. The research was carried out on the example of the chain of submesoscale eddies identified in the Tumen River water transport area in the Japan/East Sea. MODIS Aqua/Terra satellite data of the remotely sensed reflectance (Rrs) and Rrs band ratio at various wavelengths, chlorophyll-a concentration, and, for comparison, sea surface temperature (sst) were analyzed. Additionally, the results of ship surveys in September 2009 were used to study the influence of eddy vertical structure on the obtained remote characteristics. The best characteristic for detecting the studied eddies in satellite ocean color data was the MODIS chlor_a standard product, which is an estimate of chlorophyll-a concentration obtained by a combination of the three-band reflectance difference algorithm (CI) for low concentrations and the band-ratio algorithm (OCx) for high concentrations. At the same time, the weakest contrasts were in sst data due to similar water heating inside and outside the eddies. The best eddy contrast-to-noise ratio according to Rrs spectra is achieved at 547 nm in the spectral region of seawater with maximum transparency and low relative errors of measurements. The Rrs at 678 nm and associated products may be a significant characteristic for eddy detection if there are many phytoplankton in the eddy waters. The maximum depth of the remotely sensed contrast formation of the considered eddy vertical structure was ~6 m, which was significantly less than the maximum spectral penetration depth of solar radiation for remote sensing, which was in the 14–17 m range. The results obtained can be used to determine the characteristics that provide the best contrast for detecting eddy structures in remotely sensed reflectance data and to improve the interpretation of remote spectral ocean color data in the areas of eddies activity.

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Impact of Submesoscale Eddies on the Transport of Suspended Matter in the Coastal Zone of Crimea Based on Drone, Satellite, and In Situ Measurement Data

Cited by 10 publications

References 34 publications

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

Using Landsat Image Series to Identify and Characterize Persistent Oceanographic Structures in a Dynamic Marine Protected Area (North of San Jorge Gulf, Argentinian Patagonia)

Decomposition of Submesoscale Ocean Wave and Current Derived from UAV-Based Observation

Variations and Depth of Formation of Submesoscale Eddy Structures in Satellite Ocean Color Data in the Southwestern Region of the Peter the Great Bay

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