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Special Issue "Baltic Sea Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (10 September 2021) | Viewed by 13182

Special Issue Editors

Prof. Dr. Malgorzata Stramska
E-Mail Website
Guest Editor
Institute of Oceanology of the Polish Academy of Sciences, Powstancow Warszawy 55, 81-712 Sopot, Poland
Interests: ocean color; satellite oceanography; bio-optics
Prof. Agnieszka Herman
E-Mail Website
Guest Editor
Institute of Oceanography, University of Gdansk, Pilsudskiego 46, 81-378 Gdynia, Poland
Interests: sea ice; numerical modeling and remote sensing of sea ice and ocean dynamics; sea ice–ocean–atmosphere interactions
Dr. Nadia A. Kudryavtseva
E-Mail Website
Guest Editor
Department of Cybernetics, School of Science at Tallinn University of Technology, Akadeemia 21, 12618 Tallinn, Estonia
Interests: sea level, waves; satellite oceanography; extremes; coastal erosion; ocean-sea ice interaction

Special Issue Information

Dear Colleagues,

Remote Sensing, especially from satellites, is a source of invaluable data that can be used to generate long-term and synoptic information for any region of the Earth. Of special interest are coastal regions that are heavily populated and affected by industry and commerce. One of such regions is the Baltic Sea, situated in Northern Europe. It is surrounded by nine countries with about 85 million inhabitants. This means that the environmental state of the Baltic Sea affects the quality of life of a large population of Europeans.

This Special Issue will host original research papers focusing on the exploitation of remote sensing from satellites and other platforms in research and environmental monitoring of the Baltic Sea. Data from different types of sensors (optical, SAR, thermal, LIDAR, etc.), as well as different platforms on which the sensors are deployed (spaceborne, airborne, UAV) can be applied. Papers can focus on, but are not limited to atmosphere, meteorology, sea ice, color of open and coastal waters, marine primary production, monitoring of natural hazards, extreme events (storm surges), pollution, sea level changes, climate related changes, coastal erosion, river plumes, surface currents, wave patterns, etc.

Prof. Malgorzata Stramska

Prof. Agnieszka Herman

Dr. Nadia A. Kudryavtseva

Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Regional oceanography
  • Baltic Sea
  • Ocean color
  • Sea level
  • Sea ice
  • Atmospheric aerosols
  • Marine pollution
  • Sea surface temperature
  • Surface currents

Published Papers (13 papers)

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Research

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Communication
Landsat 8 Data as a Source of High Resolution Sea Surface Temperature Maps in the Baltic Sea
Remote Sens. 2021, 13(22), 4619; https://doi.org/10.3390/rs13224619 - 17 Nov 2021
Cited by 1 | Viewed by 697
Abstract
Sea surface temperature (SST) is a key hydrological variable which can be monitored via satellite. One source of thermal data with a spatial resolution high enough to study sub-mesoscale processes in coastal waters may be the Landsat mission. The Thermal Infrared Sensor on [...] Read more.
Sea surface temperature (SST) is a key hydrological variable which can be monitored via satellite. One source of thermal data with a spatial resolution high enough to study sub-mesoscale processes in coastal waters may be the Landsat mission. The Thermal Infrared Sensor on board Landsat 8 collects data in two bands, which allows for the use of the well-known nonlinear split-window formula to estimate SST (NLSST) using top-of-the-atmosphere (TOA) brightness temperature. To calibrate its coefficients a significant number of matchup points are required, representing a wide range of atmospheric conditions. In this study over 1200 granules of satellite data and 12 time series of in situ measurements from buoys and platforms operating in the Baltic Sea over a period of more than 6 years were used to select matchup points, derive NLSST coefficients and evaluate the results. To filter out pixels contaminated by clouds, ice or land influences, the IdePix algorithm was used with Quality Assessment Band and additional test of the adjacent pixels. Various combinations of flags were tested. The results show that the NLSST coefficients derived previously for coastal areas, characterised by a more humid atmosphere, might overestimate low SST values. Formulas derived for the Baltic Sea produced biases close to 0 °C and RMSEs in the range of 0.49–0.52 °C. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Chronic Oil Pollution from Vessels and Its Role in Background Pollution in the Southeastern Baltic Sea
Remote Sens. 2021, 13(21), 4307; https://doi.org/10.3390/rs13214307 - 26 Oct 2021
Cited by 1 | Viewed by 674
Abstract
The results of long-term satellite monitoring of oil pollution of the sea surface in the southeastern Baltic Sea (SEB) are discussed in this paper. From June 2004 to December 2020, in total, 2780 Synthetic Aperture Radar (SAR) images from different satellites were received [...] Read more.
The results of long-term satellite monitoring of oil pollution of the sea surface in the southeastern Baltic Sea (SEB) are discussed in this paper. From June 2004 to December 2020, in total, 2780 Synthetic Aperture Radar (SAR) images from different satellites were received and analyzed. There were 788 oil spills detected in the study area. The oil spills were concentrated along the main shipping routes in the SEB. The volume of the detected oil spills was estimated. The average size of the spill was about 2 km2 or 0.8 m3. Seasonal variability of oil pollution shows a decrease in the number of oil detections in the autumn–winter period, which is associated with the prevalence of unfavorable wind conditions that limit the use of SAR technology for oil spill detection and navigation for small ships. In situ measurements show that seasonal variation in the concentration of oil products in seawater is characterized by a maximum in April and a minimum in July. Since 2007, a decrease in oil detections has been observed for the entire Baltic Sea, including the study area. The interannual variability also shows a decrease in the concentration of oil products in the water column. In the southeastern Baltic Sea, the volume of oil products released yearly to the sea surface from ships does not exceed 0.1% of the average instantaneous presence of oil products in the water column. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
The Use of Satellite Data to Determine the Changes of Hydrodynamic Parameters in the Gulf of Gdańsk via EcoFish Model
Remote Sens. 2021, 13(18), 3572; https://doi.org/10.3390/rs13183572 - 08 Sep 2021
Viewed by 743
Abstract
Using mathematical models alone to describe the changes in the parameters characterizing the analyzed reservoir may be insufficient due to the complexity of ocean circulation. One of the ways to improve the accuracy of models is to use data assimilation based on remote [...] Read more.
Using mathematical models alone to describe the changes in the parameters characterizing the analyzed reservoir may be insufficient due to the complexity of ocean circulation. One of the ways to improve the accuracy of models is to use data assimilation based on remote sensing methods. In this study, we tested the EcoFish numerical model that was developed for the Gulf of Gdańsk area, under the FindFish Knowledge Transfer Platform. In order to improve the model results and map local phenomena occurring in the studied water, which would be difficult to simulate using only mathematical equations, EcoFish was extended with a satellite data assimilation module that assimilates the sea surface temperature data from a medium-resolution imaging spectroradiometer and an advanced ultrahigh-resolution radiometer. EcoFish was then statistically validated, which resulted in high correlations for water temperature and salinity as well as low errors in comparison with in situ experimental data. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals
Remote Sens. 2021, 13(16), 3071; https://doi.org/10.3390/rs13163071 - 04 Aug 2021
Cited by 2 | Viewed by 1453
Abstract
A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered [...] Read more.
A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a in this brackish basin, characterized by large gradients in salinity and dissolved organic matter, are hampered by its optical complexity and atmospheric correction limits. This study presents Chl-a retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances (Rrs) at ~1 km spatial resolution for the Baltic Sea. A new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented to this end. The study documents that this approach outperforms band-ratio algorithms when compared to in situ datasets, reducing the gross overestimates of Chl-a observed in the literature for this basin. The Rrs and Chl-a time series were then exploited for eutrophication monitoring, providing a quantitative description of spring and summer phytoplankton blooms in the Baltic Sea over 1998–2019. The analysis of the phytoplankton dynamics enabled the identification of the latitudinal variations in the spring bloom phenology across the basin, the early blooming in spring in the last two decades, and the description of the spatiotemporal coverage of summer cyanobacterial blooms in the central and southern Baltic Sea. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Modelling the Visibility of Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea
Remote Sens. 2021, 13(10), 1917; https://doi.org/10.3390/rs13101917 - 14 May 2021
Cited by 3 | Viewed by 652
Abstract
This paper analyses the radiance reflectance modelling of a sea area and the case of a water column polluted with an oil emulsion in relation to various depths of the occurrence of an oil-in-water emulsion in all azimuth and zenith angles. For the [...] Read more.
This paper analyses the radiance reflectance modelling of a sea area and the case of a water column polluted with an oil emulsion in relation to various depths of the occurrence of an oil-in-water emulsion in all azimuth and zenith angles. For the radiance reflectance modelling, the simulation of large numbers of solar photons in water was performed using a Monte Carlo simulation. For the simulations, the optical properties of seawater for the open sea typical of the southern Baltic Sea were used and Petrobaltic-type crude oil (extracted in the Baltic Sea) was added. Oil pollution in the sea was considered for oil droplet concentrations of 10 ppm, which were optically represented by spectral waveforms of absorption and scattering coefficients, as well as by angular light scattering distribution determined using the Mie theory. The results of the radiance reflectance modelling in the whole spectrum of both angles, azimuth and zenith, allowed us to select 555 nm as the optimal wavelength for oil emulsion detection. Moreover, the parameter contrast was defined and determined using radiance reflectance results for eight light wavelengths in the range of 412-676 nm. The contrast is discussed in relation to the various thicknesses of polluted water layers. Changes in contrast for a thickness layer 5 m under the sea surface were noted, whereas for thicker layers the contrast remained unchanged. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea
Remote Sens. 2021, 13(2), 259; https://doi.org/10.3390/rs13020259 - 13 Jan 2021
Cited by 1 | Viewed by 1339
Abstract
Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In [...] Read more.
Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
Remote Sens. 2020, 12(24), 4062; https://doi.org/10.3390/rs12244062 - 11 Dec 2020
Cited by 6 | Viewed by 1089
Abstract
Multi-mission satellite altimetry (e.g., ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic-scale view of ocean variations in past decades. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared to its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is [...] Read more.
Multi-mission satellite altimetry (e.g., ERS, Envisat, TOPEX/Poseidon, Jason) data have enabled a synoptic-scale view of ocean variations in past decades. Since 2016, the Sentinel-3 mission has provided better spatial and temporal sampling compared to its predecessors. The Sentinel-3 Ku/C Radar Altimeter (SRAL) is one of the synthetic aperture radar altimeters (SAR Altimeter) which is more precise for coastal and lake observations. The article studies the performance of the Sentinel-3 Level-2 sea level altimetry products in the coastal areas of the Baltic Sea and on two lakes of Estonia. The Sentinel-3 data were compared with (i) collocated Global Navigation Satellite System (GNSS) ship measurements, (ii) the Estonian geoid model (EST-GEOID2017) together with sea-level anomaly corrections from the tide gauges, and (iii) collocated buoy measurements. The comparisons were carried out along seven Sentinel-3A/B tracks across the Baltic Sea and Estonian lakes in 2019. In addition, the Copernicus Marine Environment Monitoring Service (CMEMS) Level-3 sea-level products and the Nucleus for European Modelling of the Ocean (NEMO) reanalysis outcomes were compared with measurements from Estonia’s 21 tide gauges and the buoy deployed offshore. Our results showed that the uncertainty of the Sentinel-3 Level-2 altimetry product was below decimetre level for the seacoast and the selected lakes of Estonia. Results from CMEMS Level-3 altimetry products showed a correlation of 0.83 (RMSE 0.18 m) and 0.91 (RMSE 0.27 m) when compared against the tide gauge measurements and the NEMO model, respectively. The overall performance of the altimetry products was very good, except in the immediate vicinity of the coastline and for the lakes, where the accuracy was nearly three times lower than for the open sea, but still acceptably good. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Remote Sensing of Ice Conditions in the Southeastern Baltic Sea and in the Curonian Lagoon and Validation of SAR-Based Ice Thickness Products
Remote Sens. 2020, 12(22), 3754; https://doi.org/10.3390/rs12223754 - 14 Nov 2020
Cited by 3 | Viewed by 1149
Abstract
Here we analyze ice conditions in the Southeastern Baltic (SEB) Sea and in the Curonian Lagoon (CL) using spaceborne synthetic aperture radar (SAR) data combined with in-situ measurements from coastal stations during four winter seasons between 2009–2013. As shown, the ice conditions in [...] Read more.
Here we analyze ice conditions in the Southeastern Baltic (SEB) Sea and in the Curonian Lagoon (CL) using spaceborne synthetic aperture radar (SAR) data combined with in-situ measurements from coastal stations during four winter seasons between 2009–2013. As shown, the ice conditions in the SEB and in the CL are strongly varying from year to year and do not always correlate with each other. In the SEB, ice cover may form only within 5–15 km band along the coast or spread up to 100 km offshore covering almost the entire region. The mean ice season duration here is 45 days. The CL is almost fully ice-covered every year apart of its northern part subjected to sea water inflow and active shipping. The ice regime is also more stable here, however, it also possesses multiple periods of partial melting and re-freezing. In this study we also perform a validation of three SAR-based ice thickness products (Envisat ASAR 0.5-km and 1-km, and RADARSAT-2 0.5-km) produced by the Finnish Meteorological Institute versus in-situ measurements in the CL. As shown, all satellite products perform rather well for the periods of gradual ice thickness growth. When the ice thickness grows rapidly, all products underestimate the observed values by 10–20 cm (20–50%). The best results were obtained for the RADARSAT-2 ice thickness product with the highest R2 value (0.68) and the root mean square error around 8 cm. The results of the study clearly show that multi-mission SAR data are very useful for spatial and temporal analysis of the ice regime in coastal waters and semi-enclosed shallow water bodies where the number of field observations is insufficient or lacking. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Chlorophyll-a Variability during Upwelling Events in the South-Eastern Baltic Sea and in the Curonian Lagoon from Satellite Observations
Remote Sens. 2020, 12(21), 3661; https://doi.org/10.3390/rs12213661 - 08 Nov 2020
Cited by 3 | Viewed by 1259
Abstract
Based on the analysis of multispectral satellite data, this work demonstrates the influence of coastal upwelling on the variability of chlorophyll-a (Chl-a) concentration in the south-eastern Baltic (SEB) Sea and in the Curonian Lagoon. The analysis of sea surface temperature (SST) data acquired [...] Read more.
Based on the analysis of multispectral satellite data, this work demonstrates the influence of coastal upwelling on the variability of chlorophyll-a (Chl-a) concentration in the south-eastern Baltic (SEB) Sea and in the Curonian Lagoon. The analysis of sea surface temperature (SST) data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua/Terra satellites, together with Chl-a maps from Medium Resolution Imaging Spectrometer (MERIS) onboard Envisat, shows a significant decrease of up to 40–50% in Chl-a concentration in the upwelling zone. This results from the offshore Ekman transport of more productive surface waters, which are replaced by cold and less-productive waters from deeper layers. Due to an active interaction between the Baltic Sea and the Curonian Lagoon which are connected through the Klaipeda Strait, coastal upwelling in the SEB also influences the hydrobiological conditions of the adjacent lagoon. During upwelling inflows, SST drops by approximately 2–8 °C, while Chl-a concentration becomes 2–4 times lower than in pre-upwelling conditions. The joint analysis of remotely sensed Chl-a and SST data reveals that the upwelling-driven reduction in Chl-a concentration leads to the temporary improvement of water quality in terms of Chl-a in the coastal zone and in the hyper-eutrophic Curonian Lagoon. This study demonstrates the benefits of multi-spectral satellite data for upscaling coastal processes and monitoring the environmental status of the Baltic Sea and its largest estuarine lagoon. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Article
Modelling Water Colour Characteristics in an Optically Complex Nearshore Environment in the Baltic Sea; Quantitative Interpretation of the Forel-Ule Scale and Algorithms for the Remote Estimation of Seawater Composition
Remote Sens. 2020, 12(17), 2852; https://doi.org/10.3390/rs12172852 - 02 Sep 2020
Cited by 4 | Viewed by 1337
Abstract
The paper presents the modelling results of selected characteristics of water-leaving light in an optically complex nearshore marine environment. The modelled quantities include the spectra of the remote-sensing reflectance Rrs(λ) and the hue angle α, which quantitatively describes the colour of [...] Read more.
The paper presents the modelling results of selected characteristics of water-leaving light in an optically complex nearshore marine environment. The modelled quantities include the spectra of the remote-sensing reflectance Rrs(λ) and the hue angle α, which quantitatively describes the colour of water visible to the unaided human eye. Based on the latter value, it is also possible to match water-leaving light spectra to classes on the traditional Forel-Ule water colour scale. We applied a simple model that assumes that seawater is made up of chemically pure water and three types of additional optically significant components: particulate organic matter (POM) (which includes living phytoplankton), particulate inorganic matter (PIM), and chromophoric dissolved organic matter (CDOM). We also utilised the specific inherent optical properties (SIOPs) of these components, determined from measurements made at a nearshore location on the Gulf of Gdańsk. To a first approximation, the simple model assumes that the Rrs spectrum can be described by a simple function of the ratio of the light backscattering coefficient to the sum of the light absorption and backscattering coefficients (u = bb/(a + bb)). The model calculations illustrate the complexity of possible relationships between the seawater composition and the optical characteristics of an environment in which the concentrations of individual optically significant components may be mutually uncorrelated. The calculations permit a quantitative interpretation of the Forel-Ule scale. The following parameters were determined for the several classes on this scale: typical spectral shapes of the u ratio, possible ranges of the total light absorption coefficient in the blue band (a(440)), as well as upper limits for concentrations of total and organic and inorganic fractions of suspended particles (SPM, POM and PIM concentrations). The paper gives examples of practical algorithms that, based on a given Rrs spectrum or some of its features, and using lookup tables containing the modelling results, enable to estimate the approximate composition of seawater. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Technical Note
Monte Carlo Radiative Transfer Simulation to Analyze the Spectral Index for Remote Detection of Oil Dispersed in the Southern Baltic Sea Seawater Column: The Role of Water Surface State
Remote Sens. 2022, 14(2), 247; https://doi.org/10.3390/rs14020247 - 06 Jan 2022
Cited by 1 | Viewed by 281
Abstract
The article presents the results of simulations that take into account the optical parameters of the selected sea region (from literature data on the southern Baltic Sea) and two optically extreme types of crude oil (from historical data) which exist in the form [...] Read more.
The article presents the results of simulations that take into account the optical parameters of the selected sea region (from literature data on the southern Baltic Sea) and two optically extreme types of crude oil (from historical data) which exist in the form of a highly watered-down oil-in-water emulsion (10 ppm). The spectral index was analyzed based on the results of modeling the radiance reflectance distribution for almost an entire hemisphere of the sky (zenith angle from 0 to 80°). The spectral index was selected and is universal for all optically different types of oil (wavelengths of 650 and 412 nm). The possibility of detecting pollution in the conditions of the wavy sea surface (as a result of wind of up to 10 m/s) was studied. It was also shown that if the viewing direction is close to a direction perpendicular to the sea surface, observations aimed at determining the spectral index are less effective than observations under the zenith angle of incidence of sunlight for all azimuths excluding the direction of sunlight’s specular reflection. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Technical Note
Modelling the Spectral Index to Detect a Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea
Remote Sens. 2021, 13(19), 3927; https://doi.org/10.3390/rs13193927 - 30 Sep 2021
Cited by 1 | Viewed by 511
Abstract
Information was obtained on the possibility of detecting oil-in-water emulsions located under the sea based on the modelling of the directional distribution of the radiance field above the water surface. The optical sea model used applies to the southern Baltic Sea, while the [...] Read more.
Information was obtained on the possibility of detecting oil-in-water emulsions located under the sea based on the modelling of the directional distribution of the radiance field above the water surface. The optical sea model used applies to the southern Baltic Sea, while the oil emulsion model is based on the optical properties of crude oil extracted in this region of the sea. The analyses were carried out while taking into account eight wavelengths in the range 412–676 nm, assuming different thicknesses of the layer contaminated with oil. The most favourable combination of two wavelengths (555/412 nm) for the determination of an index related to the polluted sea area compared to the same index for oil-free water (difference index) was identified, the value of which is indicative of the presence of the oil emulsion in water. Changes in the difference index depending on the viewing direction are shown for almost the entire upper hemisphere (zenith angles from 0° to 80°). The observation directions for which the detection of emulsions should be the most effective are shown. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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Technical Note
Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data
Remote Sens. 2021, 13(15), 3049; https://doi.org/10.3390/rs13153049 - 03 Aug 2021
Viewed by 774
Abstract
Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data [...] Read more.
Among the most frequently used satellite data are surface chlorophyll concentration (Chl) and temperature (SST). These data can be degraded in some coastal areas, for example, in the Baltic Sea. Other popular sources of data are reanalysis models. Before satellite or model data can be used effectively, they should be extensively compared with in situ measurements. Herein, we present results of such comparisons. We used SST and Chl from model reanalysis and satellites, and in situ data measured at eight open Baltic Sea stations. The data cover time interval from 1 January 1998 to 31 December 2019, but some satellite data were not always available. Both the model and the satellite SST data had good agreement with in situ measurements. In contrast, satellite and model estimates of Chl concentrations presented large errors. Modeled Chl presented the lowest bias and the best correlation with in situ data from all Chl data sets evaluated. Chl estimates from a regionally tuned algorithm (SatBaltic) had smaller errors in comparison with other satellite data sets and good agreement with in situ data in summer. Statistics were not as good for the full data set. High uncertainties found in chlorophyll satellite algorithms for the Baltic Sea highlight the importance of continuous regional validation of such algorithms with in situ data. Full article
(This article belongs to the Special Issue Baltic Sea Remote Sensing)
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