Optical Remote Sensing Applications in the Baltic Sea

2008 ◽  
pp. 91-102 ◽  
Author(s):  
H. Siegel ◽  
M. Gerth
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Optical Remote Sensing ◽  
The Baltic Sea ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
The Baltic

scholarly journals Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals

2021 ◽  
Vol 13 (16) ◽  
pp. 3071
Author(s):  
Vittorio E. Brando ◽  
Michela Sammartino ◽  
Simone Colella ◽  
Marco Bracaglia ◽  
Annalisa Di Cicco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Baltic Sea ◽  
Chlorophyll A ◽  
Atmospheric Correction ◽  
Quantitative Description ◽  
Cyanobacterial Blooms ◽  
The Baltic Sea ◽  
Sensing Applications ◽  
The Baltic

A relevant indicator for the eutrophication status in the Baltic Sea is the Chlorophyll-a concentration ( ). Alas, ocean color remote sensing applications to estimate 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 retrieval improvements for a fully reprocessed multi-sensor time series of remote-sensing reflectances () 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 observed in the literature for this basin. The and 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.

The combined use of optical remote sensing data and unattended flow‐through fluorometer measurements in the Baltic Sea

2005 ◽  
Vol 26 (2) ◽  
pp. 261-282 ◽  
Author(s):  
J. Vepsäläinen ◽  
T. Pyhälahti ◽  
E. Rantajärvi ◽  
K. Kallio ◽  
S. Pertola ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
The Baltic Sea ◽  
Sensing Data ◽  
Combined Use ◽  
Flow Through ◽  
The Baltic

scholarly journals UAV & satellite synergies for optical remote sensing applications: A literature review

2021 ◽  
Vol 3 ◽  
pp. 100019
Author(s):  
Alvarez-Vanhard Emilien ◽  
Corpetti Thomas ◽  
Houet Thomas
Keyword(s):  
Remote Sensing ◽  
Literature Review ◽  
Optical Remote Sensing ◽  
Sensing Applications ◽  
Remote Sensing Applications

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 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.

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