Using MERIS data to assess the spatial and temporal variability of phytoplankton in coastal areas

Beltrán-Abaunza, José M.; Kratzer, Susanne; Höglander, Helena

doi:10.1080/01431161.2016.1249307

Cited by 11 publications

(7 citation statements)

References 45 publications

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“…Both the satellite data and the modelled data in the Bråviken study indicate a spring and a summer bloom ( Figure 12). However, the modelled summer bloom shows higher CHL-a values than during spring, and therefore does not quite correctly depict the phenology in the Baltic proper which usually consists of a large phytoplankton spring peak and a somewhat lower summer peak of filamentous cyanobacteria [21,73]. The results indicate that the model may need improved parametrization, e.g., by modulating the seasonal light attenuation as already discussed above.…”

Section: Potential Advantages and Challenges For Monitoring Water Quamentioning

confidence: 96%

“…Also, such infrequent in situ measurements are not able to depict phytoplankton phenology correctly and this cannot be used as a tool to adequately assess changes in phenology. Having said that, there are also more frequent coastal monitoring programs, as for example in Himmerfärden bay, where both satellite data and in situ data can depict phytoplankton phenology as well as CHL-a anomalies [21].…”

Section: Potential Advantages and Challenges For Monitoring Water Quamentioning

confidence: 99%

“…To fully capture the variability, monitoring should be performed on a monthly basis throughout the entire season, and if possible weekly sampling during times of phytoplankton blooms. However, the stations that are included in monitoring programs might still not fully capture the spatial and temporal variability of a waterbody [19][20][21]. Moreover, due to costs associated with in situ measurements, CHL-a samples are often taken only during the summer months (e.g., to track the development of the cyanobacterial blooms) once a month.…”

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confidence: 99%

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Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

et al. 2019

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Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002 were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R 2 : 0.58-0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.Sea eutrophication is recognized as a major large-scale environmental pressure, partially attributed to an excess loading of anthropogenically-derived nutrients [2][3][4]. In marine systems, eutrophication occurs predominantly in coastal areas and semi-enclosed waterbodies [2,5,6]. Nutrient loads to coastal areas have significantly increased in recent decades due to population growth especially in coastal areas. In the Baltic Sea the problem is exalted due to the relatively large catchment area of the Baltic Sea (i.e., 4.2 times the Baltic Sea), and a relatively low water exchange rate with the North Sea. For the last three decades, it is has been acknowledged that excessive amounts of nutrients such as N, P and organic matter-often represented by organic particular carbon (POC)-result in anoxic bottom waters, the spreading of dead bottom zones and increased frequency and intensity of algal...

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Section: Potential Advantages and Challenges For Monitoring Water Quamentioning

confidence: 96%

Section: Potential Advantages and Challenges For Monitoring Water Quamentioning

confidence: 99%

mentioning

confidence: 99%

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Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

et al. 2019

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“…The higher range of values in coastal waters can be attributed to the influence from land (erosion and run-off). The strong variability of TSM in the open sea during summer can be mostly explained by the variability in cyanobacteria biomass (Kratzer and Tett, 2009;Beltrán-Abaunza et al, 2016;Kari et al, 2017).…”

Section: Trend In Himmerfjärden Baymentioning

confidence: 99%

“…The remote sensing approach also allows to retrieve and assess the variability and distribution of TSM concentrations between stations. As in situ measurements are relatively expensive and much more time consuming, one usually samples at a much lower spatial, and temporal resolution than possible to retrieve from satellite data (Beltrán-Abaunza et al, 2016;Harvey et al, 2018). Additionally, similar transects derived in other regions of the Baltic Sea allow to spatially evaluate the contribution of river run-off and coastal resuspension (Kyryliuk, 2014).…”

Section: Trend In Himmerfjärden Baymentioning

confidence: 99%

Summer Distribution of Total Suspended Matter Across the Baltic Sea

Kyryliuk

Kratzer

2019

Front. Mar. Sci.

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There are three optical in-water components that, besides water itself, govern the underwater light field: phytoplankton, total suspended matter (TSM), and colored dissolved organic matter (CDOM). In essence, it is the spectral absorption and scattering properties of each optical component that govern the underwater light field, and also the color of the sea that we can perceive, and that can also be measured remotely from space. The Baltic Sea is optically dominated by CDOM, apart from cyanobacteria blooms that often cover most of the Baltic proper during summer. Remote sensing images of TSM reveal large-and mesoscale features and currents, especially in the Southern Baltic, which are influenced both by atmospheric Rossby waves and the Coriolis force. In coastal waters, the optical properties are strongly influenced by inorganic suspended matter, which may originate from coastal erosion and from runoff from land, streams, and rivers. In this paper, we evaluate the distribution of TSM across the Baltic Sea using remote sensing data and statistically compare the TSM loads in the different Helsinki Commission (HELCOM)-defined basins. The total suspended matter (TSM) loads during summer vary substantially in the different basins, with the southeastern Baltic overall being most influenced by cyanobacteria blooms. The Gdansk basin and the Gulf of Riga were distinguished both by relatively high TSM loads with high standard deviations, indicating strong fluvial input and/or resuspension of sediments. We also evaluate a coastal TSM transect in Himmerfjärden bay, which is located at the Swedish East coast in the Western Gotland Basin. The effect of wind-wave stirring on the distribution of TSM from source (shore) to sink (open sea) can be assessed using satellite data from European Space Agency's (ESA) MEdium Resolution Imaging Spectrometer (MERIS) mission (2002-2012) with 300 m resolution. The TSM transect data from areas with low wind exposure and a stable thermocline showed a gradient distribution perpendicular to the coast for summer seasons 2009, 2010, 2011, and a 3-year summer composite, confirming a previous bio-optical study from the Western Gotland basin.

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Associations between county-level land cover classes and cyanobacteria blooms in the United States

Marion

Zhang

Cutting

et al. 2017

Ecological Engineering

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Using MERIS data to assess the spatial and temporal variability of phytoplankton in coastal areas

Abstract: This study aims to highlight how satellite data can be used for an improved understanding of ecological processes in a narrow coastal bay.

Cited by 11 publications

References 45 publications

Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

Synergy of Satellite, In Situ and Modelled Data for Addressing the Scarcity of Water Quality Information for Eutrophication Assessment and Monitoring of Swedish Coastal Waters

Summer Distribution of Total Suspended Matter Across the Baltic Sea

Associations between county-level land cover classes and cyanobacteria blooms in the United States

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