scholarly journals Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3609 ◽  
Author(s):  
Kyryliuk ◽  
Kratzer
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Attenuation Coefficient ◽  
Secchi Depth ◽  
Local Algorithm ◽  
The Baltic Sea ◽  
In Situ Data ◽  
The Baltic ◽  
Level 2

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient Kd(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (Rrs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data.

scholarly journals Satellite Remote Sensing Signatures of the Major Baltic Inflows

2019 ◽  
Vol 11 (8) ◽  
pp. 954
Author(s):  
Malgorzata Stramska ◽  
Paulina Aniskiewicz
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Large Scale ◽  
Spatial Information ◽  
The Baltic Sea ◽  
Salty Water ◽  
In Situ Data ◽  
The North ◽  
The Baltic

Variability of sea level in the North and Baltic Seas, enforced by weather patterns, affects the intensity of water exchange between these seas. Transfer of salty water from the North Sea is very important for the hydrography of the Baltic Sea. The volume of inflowing salty water can occasionally increase remarkably. Such incidents, called the Major Baltic Inflows (MBIs), are unpredictable, of relatively short duration, and difficult to observe using in situ data. We have shown that remote sensing altimetry can be used as a complementary source of information about the MBI events. The advantage of using such data is that large-scale spatial information about SLA is available with daily resolution. We have described changes in SLA during several MBI events observed in 1993–2017. The net volume of water transported into the Baltic Sea varied between the events due to differences in atmospheric forcing. Based on SLA data, the largest inflow of water happened during the 2014 MBI. This is in agreement with previously published results, based on in situ data.

scholarly journals Comparisons of Satellite and Modeled Surface Temperature and Chlorophyll Concentrations in the Baltic Sea with In Situ Data

2021 ◽  
Vol 13 (15) ◽  
pp. 3049
Author(s):  
Malgorzata Stramska ◽  
Marta Konik ◽  
Paulina Aniskiewicz ◽  
Jaromir Jakacki ◽  
Miroslaw Darecki
Keyword(s):  
Baltic Sea ◽  
Satellite Data ◽  
In Situ Measurements ◽  
Data Sets ◽  
The Baltic Sea ◽  
Data Set ◽  
In Situ Data ◽  
The Baltic ◽  
Good Agreement

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.

scholarly journals Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multisensor data

2015 ◽  
Vol 12 (5) ◽  
pp. 2283-2313
Author(s):  
J. Pitarch ◽  
G. Volpe ◽  
S. Colella ◽  
H. Krasemann ◽  
R. Santoleri
Keyword(s):  
Time Series ◽  
Baltic Sea ◽  
Late Winter ◽  
The Baltic Sea ◽  
In Situ Data ◽  
Basin Scale ◽  
Gulf Of Bothnia ◽  
Summer Bloom ◽  
The Baltic

Abstract. Fifteen-year (1997–2012) time series of chlorophyll a (CHL) in the Baltic Sea, based on merged multisensor satellite data provided by the European projects Globcolour and ESA-OC-CCI were analysed. Several available CHL algorithms were sea-truthed against a large in situ CHL dataset consisting of data by Seadatanet, HELCOM and NOAA. Matchups were calculated for three separate areas (1) Skagerrak and Kattegat, (2) Baltic Proper plus gulfs of Riga and Finland, called here "Central Baltic", (3) Gulf of Bothnia, and for the three areas as a whole. Statistics showed low linearity. The OC4v6 algorithm (R2 = 0.46, BIAS = +60 %, RMS = 79 % for the whole dataset) was linearly transformed by using the best linear fit (OC4corr). By construction, the bias was corrected, but RMS was increased instead. Despite this shortcoming, we demonstrated that errors between OC4corr and in situ data were log-normally distributed and centred at zero. Consequently, unbiased estimators of the horizontally-averaged CHL could be obtained, the error of which tends to zero when a large amount of pixels is averaged. From the basin-wide time series, the climatology and the annual anomalies were separated. The climatologies revealed completely different CHL dynamics among regions: in Skagerrak and Kattegat, CHL strongly peaks in late winter, with a minimum in summer and a secondary peak in spring. In the Central Baltic, CHL follows a dynamics of a spring CHL peak, followed by a much stronger summer bloom, with decreasing CHL towards winter. The Gulf of Bothnia shows a similar CHL dynamics as the central Baltic, although the summer bloom is absent. Across years, CHL showed great variability. Supported by auxiliary satellite sea-surface temperature (SST) data, we found that phytoplankton growth was inhibited in the central Baltic Sea in the years of colder summers or when the SST happened to increase later in the season. Extremely high CHL in spring 2008 was detected and linked to an exceptionally warm preceding winter. Sharp SST changes were found to induce CHL changes in the same direction. This phenomenon was appreciated best by overlaying the time series of the CHL and SST anomalies.

scholarly journals Testing the performance of empirical remote sensing algorithms in the Baltic Sea waters with modelled and in situ reflectance data

Oceanologia ◽  
2017 ◽  
Vol 59 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Martin Ligi ◽  
Tiit Kutser ◽  
Kari Kallio ◽  
Jenni Attila ◽  
Sampsa Koponen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
The Baltic Sea ◽  
The Baltic ◽  
Reflectance Data

scholarly journals Consistency between Satellite Ocean Colour Products under High Coloured Dissolved Organic Matter Absorption in the Baltic Sea

2021 ◽  
Vol 14 (1) ◽  
pp. 89
Author(s):  
Gavin H. Tilstone ◽  
Silvia Pardo ◽  
Stefan G. H. Simis ◽  
Ping Qin ◽  
Nick Selmes ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Atmospheric Correction ◽  
Relative Difference ◽  
Global Ocean ◽  
Ocean Colour ◽  
The Baltic Sea ◽  
Chl A ◽  
The Baltic

Ocean colour (OC) remote sensing is an important tool for monitoring phytoplankton in the global ocean. In optically complex waters such as the Baltic Sea, relatively efficient light absorption by substances other than phytoplankton increases product uncertainty. Sentinel-3 OLCI-A, Suomi-NPP VIIRS and MODIS-Aqua OC radiometric products were assessed using Baltic Sea in situ remote sensing reflectance (Rrs) from ferry tracks (Alg@line) and at two Aerosol Robotic Network for Ocean Colour (AERONET-OC) sites from April 2016 to September 2018. A range of atmospheric correction (AC) processors for OLCI-A were evaluated. POLYMER performed best with <23 relative % difference at 443, 490 and 560 nm compared to in situ Rrs and 28% at 665 nm, suggesting that using this AC for deriving Chl a will be the most accurate. Suomi-VIIRS and MODIS-Aqua underestimated Rrs by 35, 29, 22 and 39% and 34, 22, 17 and 33% at 442, 486, 560 and 671 nm, respectively. The consistency between different AC processors for OLCI-A and MODIS-Aqua and VIIRS products was relatively poor. Applying the POLYMER AC to OLCI-A, MODIS-Aqua and VIIRS may produce the most accurate Rrs and Chl a products and OC time series for the Baltic Sea.

scholarly journals Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Mean Squared Error ◽  
Numerical Models ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Chl A ◽  
Marginal Seas ◽  
The Baltic

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.

scholarly journals In-Situ Evaluation of the Protectivity of Coatings Applied to Metal Cultural Artefacts Using Non-Destructive Electrochemical Measurements

2021 ◽  
Vol 2 (1) ◽  
pp. 120-132
Author(s):  
Douglas J. Mills ◽  
Katarzyna Schaefer ◽  
Tomasz Wityk
Keyword(s):  
Baltic Sea ◽  
Electrochemical Noise ◽  
Organic Coating ◽  
Noise Measurement ◽  
The Baltic Sea ◽  
Coating Type ◽  
The Baltic ◽  
Non Destructive ◽  
Electrolytic Resistance

Electrochemical Noise Measurement (ENM) and DC electrolytic resistance measurement (ERM) can be used to assess the level of protectiveness provided by an organic coating (paint or varnish) to the underlying metal. These techniques also have applicability to the thinner, transparent type of coatings used to protect archaeological artefacts. Two studies are presented here demonstrating how ERM and ENM techniques can be applied in artefact preservation. The similarity of the techniques, both of which are a measure of resistance, means results can be considered to be analogous. The first study investigated the use of ERM to determine the protection levels provided by typical coatings in order to develop a database of coating type and application for objects, for specific environments. The second study used ENM to evaluate coatings which had been applied to historic artefacts recovered from shipwrecks in the Baltic Sea and displayed inside the museum or kept in the museum store area. The studies showed the usefulness of both techniques for determining the level of protection of a coating and how a better performing coating can be specified if a pre-existing coating on an artefact has been found to be unsuitable.

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