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 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.

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.

Sea level variations in the coastal region from altimetry – Using optimal interpolation in the Baltic Sea for 3-day mean sea level from altimetry, tide gauge and model data

2021 ◽  
Author(s):  
Ida Margrethe Ringgaard ◽  
Jacob L. Høyer ◽  
Kristine S. Madsen ◽  
Adili Abulaitijiang ◽  
Ole B. Andersen
Keyword(s):  
Baltic Sea ◽  
Sea Level ◽  
Temporal Resolution ◽  
Satellite Altimetry ◽  
Tide Gauge ◽  
Coastal Region ◽  
Optimal Interpolation ◽  
The Baltic Sea ◽  
Spatial Coverage ◽  
The Baltic

&lt;p&gt;The rise and fall of the sea surface in the coastal region is observed closely by two different sources: tide gauges measure the relative sea level anomaly at the coast at high temporal resolution (minutes or hours) and satellite altimeters measure the absolute sea surface height of the open ocean along tracks multiple times a day. However, these daily tracks are scattered across the Baltic Sea with each track being repeated at a lower temporal resolution (days). Due to the inverse relationship between spatial and temporal coverage of the satellite altimetry data, gridded satellite altimetry products often prioritize spatial coverage over temporal resolution, thus filtering out the high sea level variability. In other words, the satellite data, and especially averaged products, often miss the daily sea level variability, such as storm surges, which is most important for all societies in the coastal region. To compensate for the sparse spatial coverage from satellite altimetry, we here present an experimental product developed as part of the ESA project Baltic+SEAL: &amp;#160;on a 3-day scale, the DMI Optimal Interpolation (DMI-OI) method is combined with error statistics from a storm surge model as well as 3-day averages from both tide gauge observations and satellite altimetry tracks to generate a gridded sea level anomaly product for the Baltic Sea for year 2017. The product captures the overall temporal evolution of the sea level changes well for most areas with an average RMSE wrt. tide gauge observations of 17.2 cm and a maximum of 34.2 cm. Thus, the 3-day mean gridded product shows potential as an alternative to monthly altimetry products, although further work is needed.&lt;/p&gt;

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 Geoelectric Monitoring of Wind-Driven Barotropic Transports in the Baltic Sea

2007 ◽  
Vol 24 (9) ◽  
pp. 1655-1664 ◽  
Author(s):  
Jenny A. U. Nilsson ◽  
Peter Sigray ◽  
Robert H. Tyler
Keyword(s):  
Baltic Sea ◽  
Circulation Model ◽  
Correlation Study ◽  
The Baltic Sea ◽  
Barotropic Flow ◽  
Spatial Coverage ◽  
Baltic Proper ◽  
The Baltic ◽  
Scale Motion ◽  
Geoelectric Monitoring

Abstract The possibility of using data from a cable-based observational system for long-term monitoring of barotropic flow in the Baltic Sea was investigated. Measurements were made of the induced potential differences between Visby on the island of Gotland and Västervik on the Swedish mainland and a yearlong period was studied in order to ensure the presence of seasonal fluctuations. The predictions from a 2D electric-potential model, forced by velocity fields from a shallow-water circulation model, proved to be well correlated with the observations. A winter and a summer period were selected for a thorough analysis, the results of which indicated a stronger correlation during winter. This implies that the relative importance of the barotropic forcing tends to weaken during summer. The spatial coverage of the induced potential differences for the cable region was found to encompass a considerable part of the Baltic proper. The correlation study indicated that the winter circulation in the Baltic proper showed “broad-scale” motion, whereas summer conditions were characterized by a barotropic gyre. An overall result of the investigation is that geoelectric monitoring is capable of providing useful data for oceanographic purposes.

Comparison of the main sea ice parameters received from satellite measurements and based on numerical simulation for the Baltic Sea region

2020 ◽  
Author(s):  
Jaromir Jakacki ◽  
Maciej Muzyka ◽  
Marta Konik ◽  
Anna Przyborska ◽  
Jan Andrzejewski
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Baltic Sea ◽  
Melting Time ◽  
Ice Thickness ◽  
Satellite Measurements ◽  
The Baltic Sea ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Baltic

&lt;p&gt;During the last decades remote sensing observations as well as modelling tools has been developed and become key elements of oceanographic research. One of the main advantages of both tools is a possibility of measuring large-scale areas.&lt;/p&gt;&lt;p&gt;The remote sensing measurements deliver only snapshots of the ice situation with no information about backgroundconditions. Moreover, providing picture of the whole area requires sometimes combining various datasets that increases uncertainties. &amp;#160;Modelling simulations provide full history of external conditions, but they also introduce errors that are the result of parameterizations. Also, an inaccuracy provided by forcing fields at the top and bottom boundaries are accumulated in the model.&lt;/p&gt;&lt;p&gt;In this work sea ice parameters such as sea ice concentration, thickness and volume obtained from both &amp;#8211; satellite measurements and modelling has been compared. Numerical simulations were performed using standalone Community Ice Code (CICE) model (v. 6.0). It is a descendant of the basin scale dynamic-thermodynamic and thickness distribution sea ice model. The model is well known by scientific community and was widely used in a global as well as regional research, even operationally. The satellite derived ice thickness products were based on the C band HH-polarized SAR measurements originating from the satellites Sentinel-1 and RADARSAT-2. The sea ice concentration maps contain also visual and infrared information from MODIS and NOAA.&lt;/p&gt;&lt;p&gt;The ice extent, thickness and volume were compared in several regions within the Baltic Sea.&amp;#160; Seasonal changes were analyzed with a particular attention to ice formation and melting time. The sea ice extent datasets were compatible. Inconsistencies were observed for the sea ice thickness delivered by satellite measurements, especially during the ice melt. The work presents direction for ignoring satellite data with an error related to ice melting that allows for excluding erroneous satellite maps and obtain reliable intercalibration.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;This work was partly funded by Polish National Science Centre, project number 2017/25/B/ST10/00159&lt;/p&gt;

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