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.

Mapping seagrass and water depths using Sentinel-2

2020 ◽  
Author(s):  
Katja Kuhwald ◽  
Philipp Held ◽  
Florian Gausepohl ◽  
Jens Schneider von Deimling ◽  
Natascha Oppelt
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Atmospheric Correction ◽  
The Baltic Sea ◽  
Seagrass Meadows ◽  
Spatial Coverage ◽  
Acoustic Methods ◽  
The Baltic ◽  
Water Depths ◽  
Sentinel 2

<p>Seagrass meadows cover large benthic areas of the Baltic Sea, but eutrophication and climate change imply declining seagrass coverage. Apart from acoustic methods and traditional diver mappings, optical remote sensing techniques allow for mapping seagrass. Optical satellite analyses of seagrass mapping may supplement acoustic methods in shallow coastal waters with observations that are more frequent and have a larger spatial coverage.</p><p>In the clear Greek Mediterranean Sea, Sentinel-2 was already applied successfully to detect bathymetry and seagrass meadows. We are now testing whether Sentinel-2 data are also suitable for analysing the sublittoral in the turbid waters of the Baltic Sea. We focus on an extensive shallow water area near Kiel/Germany. Based on Sentinel-2 data, we analyse water depth and differentiate between seagrass covered and bare sandy ground. We derive these parameters using empirical and process-based models. First results show that Sentinel-2 allows to determine water depths up to 4 m (RMSE ~ 0.2 m). Comparisons with LiDAR water depths show that inaccuracies increase in overgrown areas. Our study also shows that the atmospheric correction algorithm influences sublittoral ground mappings with Sentinel-2 data. For instance, the absolute water depths of the process-based modelling differ up to 2.5 m on average depending on the atmospheric correction algorithm (ACOLITE, Sen2Cor, iCOR).</p><p>Comparing Sentinel-2 seagrass classifications with diver mappings and aerial imagery emphasises that empiric approaches provide plausible sublittoral ground classifications up to approximately 4 m water depth. Combining these results with seagrass mappings based on acoustic measurements (deeper than 4 m water) provides a synthesised sublittoral classification map of the study area up to the present growth limit of seagrass (~ 7 m in the study area).</p><p>The Baltic Sea is considered as a very turbid environment, nevertheless we show that satellite-based remote sensing has a great potential for shedding light into the  "white ribbon". The spatial coverage and temporal resolution of the analysed Sentinel-2 data increases the knowledge about the occurrence of seagrass and its spatio-temporal dynamics. Nevertheless, the influence of the selected atmospheric correction approach on the results shows that further research in remote sensing is necessary to assess seagrass meadows reliably.</p>

scholarly journals Multidecadal time series of satellite-detected accumulations of cyanobacteria in the Baltic Sea

2014 ◽  
Vol 11 (13) ◽  
pp. 3619-3633 ◽  
Author(s):  
M. Kahru ◽  
R. Elmgren
Keyword(s):  
Time Series ◽  
Baltic Sea ◽  
Chlorophyll A ◽  
Interannual Variations ◽  
The Baltic Sea ◽  
Decadal Scale ◽  
Long Time ◽  
Areal Fraction ◽  
Extensive Surface ◽  
The Baltic

Abstract. Cyanobacteria, primarily of the species \\textit{Nodularia spumigena}, form extensive surface accumulations in the Baltic Sea in July and August, ranging from diffuse flakes to dense surface scums. The area of these accumulations can reach ~ 200 000 km2. We describe the compilation of a 35-year-long time series (1979–2013) of cyanobacteria surface accumulations in the Baltic Sea using multiple satellite sensors. This appears to be one of the longest satellite-based time series in biological oceanography. The satellite algorithm is based on remote sensing reflectance of the water in the red band, a measure of turbidity. Validation of the satellite algorithm using horizontal transects from a ship of opportunity showed the strongest relationship with phycocyanin fluorescence (an indicator of cyanobacteria), followed by turbidity and then by chlorophyll a fluorescence. The areal fraction with cyanobacteria accumulations (FCA) and the total accumulated area affected (TA) were used to characterize the intensity and extent of the accumulations. The fraction with cyanobacteria accumulations was calculated as the ratio of the number of detected accumulations to the number of cloud-free sea-surface views per pixel during the season (July–August). The total accumulated area affected was calculated by adding the area of pixels where accumulations were detected at least once during the season. The fraction with cyanobacteria accumulations and TA were correlated (R2 = 0.55) and both showed large interannual and decadal-scale variations. The average FCA was significantly higher for the second half of the time series (13.8%, 1997–2013) than for the first half (8.6%, 1979–1996). However, that does not seem to represent a long-term trend but decadal-scale oscillations. Cyanobacteria accumulations were common in the 1970s and early 1980s (FCA between 11–17%), but rare (FCA below 4%) during 1985–1990; they increased again starting in 1991 and particularly in 1999, reaching maxima in FCA (~ 25%) and TA (~ 210 000 km2) in 2005 and 2008. After 2008, FCA declined to more moderate levels (6–17%). The timing of the accumulations has become earlier in the season, at a mean rate of 0.6 days per year, resulting in approximately 20 days advancement during the study period. The interannual variations in FCA are positively correlated with the concentration of chlorophyll a during July–August sampled at the depth of ~ 5 m by a ship of opportunity, but interannual variations in FCA are more pronounced as the coefficient of variation is over 5 times higher.

scholarly journals Satellite detection of multi-decadal time series of cyanobacteria accumulations in the Baltic Sea

2014 ◽  
Vol 11 (2) ◽  
pp. 3319-3364 ◽  
Author(s):  
M. Kahru ◽  
R. Elmgren
Keyword(s):  
Time Series ◽  
Baltic Sea ◽  
Chlorophyll A ◽  
Interannual Variations ◽  
The Baltic Sea ◽  
Decadal Scale ◽  
Long Time ◽  
Areal Fraction ◽  
Extensive Surface ◽  
The Baltic

Abstract. Cyanobacteria, primarily of the species Nodularia spumigena, form extensive surface accumulations in the Baltic Sea in July and August, ranging from diffuse flakes to dense surface scum. We describe the compilation of a 35 year (1979–2013) long time series of cyanobacteria surface accumulations in the Baltic Sea using multiple satellite sensors. This appears to be one of the longest satellite-based time series in biological oceanography. The satellite algorithm is based on increased remote sensing reflectance of the water in the red band, a measure of turbidity. Validation of the satellite algorithm using horizontal transects from a ship of opportunity showed the strongest relationship with phycocyanin fluorescence (an indicator of cyanobacteria), followed by turbidity and then by chlorophyll a fluorescence. The areal fraction with cyanobacteria accumulations (FCA) and the total accumulated area affected (TA) were used to characterize the intensity and extent of the accumulations. FCA was calculated as the ratio of the number of detected accumulations to the number of cloud free sea-surface views per pixel during the season (July–August). TA was calculated by adding the area of pixels where accumulations were detected at least once during the season. FCA and TA were correlated (R2 = 0.55) and both showed large interannual and decadal-scale variations. The average FCA was significantly higher for the 2nd half of the time series (13.8%, 1997–2013) than for the first half (8.6%, 1979–1996). However, that does not seem to represent a long-term trend but decadal-scale oscillations. Cyanobacteria accumulations were common in the 1970s and early 1980s (FCA between 11–17%), but rare (FCA below 4%) from 1985 to 1990; they increased again from 1991 and particularly from 1999, reaching maxima in FCA (~ 25%) and TA (~ 210 000 km2) in 2005 and 2008. After 2008 FCA declined to more moderate levels (6–17%). The timing of the accumulations has become earlier in the season, at a~mean rate of 0.6 days per year, resulting in approximately 20 days advancement during the study period. The interannual variations in FCA are positively correlated with the concentration of chlorophyll a in July–August sampled at the depth of ~ 5 m by a ship of opportunity program, but interannual variations in FCA are more pronounced.

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.

The History of Cyanobacterial Blooms in the Baltic Sea

AMBIO ◽  
2001 ◽  
Vol 30 (4) ◽  
pp. 172-178 ◽  
Author(s):  
Terttu Finni ◽  
Kaisa Kononen ◽  
Riitta Olsonen ◽  
Kerstin Wallström
Keyword(s):  
Baltic Sea ◽  
Cyanobacterial Blooms ◽  
The Baltic Sea ◽  
History Of ◽  
The Baltic
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