scholarly journals Spatiotemporal patterns of N<sub>2</sub> fixation in coastal waters derived from rate measurements and remote sensing

2020 ◽  
Author(s):  
Mindaugas Zilius ◽  
Irma Vybernaite-Lubiene ◽  
Diana Vaiciute ◽  
Donata Overlingė ◽  
Evelina Grinienė ◽  
...  
Keyword(s):  
Remote Sensing ◽  
N2 Fixation ◽  
Phytoplankton Community ◽  
Coastal Lagoons ◽  
Curonian Lagoon ◽  
Strong Correlations ◽  
N Budget ◽  
Chl A ◽  
Heterocystous Cyanobacteria ◽  
N Removal

Abstract. Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flosaquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll-a (Chl-a) and N2 fixation. We used regression models relating N2 fixation to Chl-a, along with remote sensing-based estimates of Chl-a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment-water exchange and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl-a with empirical models relating N2 fixation rates to Chl-a.

scholarly journals Spatiotemporal patterns of N<sub>2</sub> fixation in coastal waters derived from rate measurements and remote sensing

2021 ◽  
Vol 18 (5) ◽  
pp. 1857-1871
Author(s):  
Mindaugas Zilius ◽  
Irma Vybernaite-Lubiene ◽  
Diana Vaiciute ◽  
Donata Overlingė ◽  
Evelina Grinienė ◽  
...  
Keyword(s):  
Remote Sensing ◽  
N2 Fixation ◽  
Phytoplankton Community ◽  
Coastal Lagoons ◽  
Curonian Lagoon ◽  
Strong Correlations ◽  
N Budget ◽  
Chl A ◽  
Heterocystous Cyanobacteria ◽  
N Removal

Abstract. Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

Particulate light absorption and the prediction of phytoplankton biomass and planktonic metabolism in northeastern Spanish aquatic ecosystems

2000 ◽  
Vol 57 (1) ◽  
pp. 25-33 ◽  
Author(s):  
C M Duarte ◽  
S Agustí ◽  
J Kalff
Keyword(s):  
Absorption Coefficient ◽  
Light Absorption ◽  
Aquatic Ecosystems ◽  
Coastal Lagoons ◽  
Strong Relationship ◽  
Community Respiration ◽  
Chl A ◽  
Light Absorption Coefficient ◽  
Alpine Rivers ◽  
Detrital Particles

Examination of particulate light absorption and microplankton metabolism in 36 northeastern Spanish aquatic ecosystems, ranging from alpine rivers to inland saline lakes and the open Mediterranean Sea, revealed the existence of general relationships between particulate light absorption and the biomass of phytoplankton and microplankton metabolism. The particulate absorption spectra reflected a dominance of nonphotosynthetic, likely detrital, particles in rivers and a dominance of phytoplankton in coastal lagoons. There was a strong relationship between the light absorbed by phytoplankton and the chlorophyll a (Chl a) concentration of the systems, which indicated an average (±SE) Chl a specific absorption coefficient of 0.0233 ± 0.0020 m2·mg Chl a-1 for these widely diverse systems. Chl a concentration was a weaker predictor of the total particulate light absorption coefficient, pointing to an important role of nonphytoplanktonic particles in light absorption. Gross production was very closely related to the light absorption coefficient of phytoplankton, whereas community respiration was strongly correlated with the total particulate light absorption coefficient, indicating the optical signatures of sestonic particles to be reliable predictors of planktonic biomass and metabolism in aquatic ecosystems.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

scholarly journals Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Phytoplankton Bloom ◽  
Ocean Color ◽  
Coastal Region ◽  
Coastal Areas ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
Peak Biomass ◽  
Phytoplankton Communities

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

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 Ocean acidification of a coastal Antarctic marine microbial community reveals a critical threshold for CO<sub>2</sub> tolerance in phytoplankton productivity

2018 ◽  
Vol 15 (1) ◽  
pp. 209-231 ◽  
Author(s):  
Stacy Deppeler ◽  
Katherina Petrou ◽  
Kai G. Schulz ◽  
Karen Westwood ◽  
Imojen Pearce ◽  
...  
Keyword(s):  
Organic Matter ◽  
Ocean Acidification ◽  
Primary Productivity ◽  
Phytoplankton Community ◽  
Bacterial Abundance ◽  
Critical Threshold ◽  
Bacterial Productivity ◽  
High Co2 ◽  
Chl A ◽  
Antarctic Marine

Abstract. High-latitude oceans are anticipated to be some of the first regions affected by ocean acidification. Despite this, the effect of ocean acidification on natural communities of Antarctic marine microbes is still not well understood. In this study we exposed an early spring, coastal marine microbial community in Prydz Bay to CO2 levels ranging from ambient (343 µatm) to 1641 µatm in six 650 L minicosms. Productivity assays were performed to identify whether a CO2 threshold existed that led to a change in primary productivity, bacterial productivity, and the accumulation of chlorophyll a (Chl a) and particulate organic matter (POM) in the minicosms. In addition, photophysiological measurements were performed to identify possible mechanisms driving changes in the phytoplankton community. A critical threshold for tolerance to ocean acidification was identified in the phytoplankton community between 953 and 1140 µatm. CO2 levels  ≥ 1140 µatm negatively affected photosynthetic performance and Chl a-normalised primary productivity (csGPP14C), causing significant reductions in gross primary production (GPP14C), Chl a accumulation, nutrient uptake, and POM production. However, there was no effect of CO2 on C : N ratios. Over time, the phytoplankton community acclimated to high CO2 conditions, showing a down-regulation of carbon concentrating mechanisms (CCMs) and likely adjusting other intracellular processes. Bacterial abundance initially increased in CO2 treatments  ≥ 953 µatm (days 3–5), yet gross bacterial production (GBP14C) remained unchanged and cell-specific bacterial productivity (csBP14C) was reduced. Towards the end of the experiment, GBP14C and csBP14C markedly increased across all treatments regardless of CO2 availability. This coincided with increased organic matter availability (POC and PON) combined with improved efficiency of carbon uptake. Changes in phytoplankton community production could have negative effects on the Antarctic food web and the biological pump, resulting in negative feedbacks on anthropogenic CO2 uptake. Increases in bacterial abundance under high CO2 conditions may also increase the efficiency of the microbial loop, resulting in increased organic matter remineralisation and further declines in carbon sequestration.

scholarly journals Nitrogen cycling in Sandusky Bay, Lake Erie: oscillations between strong and weak export and implications for harmful algal blooms

2018 ◽  
Vol 15 (9) ◽  
pp. 2891-2907 ◽  
Author(s):  
Kateri R. Salk ◽  
George S. Bullerjahn ◽  
Robert Michael L. McKay ◽  
Justin D. Chaffin ◽  
Nathaniel E. Ostrom
Keyword(s):  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Lake Erie ◽  
N Uptake ◽  
N Fixation ◽  
N Budget ◽  
N2o Production ◽  
N Removal ◽  
Uptake Rates ◽  
N Loading

Abstract. Recent global water quality crises point to an urgent need for greater understanding of cyanobacterial harmful algal blooms (cHABs) and their drivers. Nearshore areas of Lake Erie such as Sandusky Bay may become seasonally limited by nitrogen (N) and are characterized by distinct cHAB compositions (i.e., Planktothrix over Microcystis). This study investigated phytoplankton N uptake pathways, determined drivers of N depletion, and characterized the N budget in Sandusky Bay. Nitrate (NO3-) and ammonium (NH4+) uptake, N fixation, and N removal processes were quantified by stable isotopic approaches. Dissimilatory N reduction was a relatively modest N sink, with denitrification, anammox, and N2O production accounting for 84, 14, and 2 % of sediment N removal, respectively. Phytoplankton assimilation was the dominant N uptake mechanism, and NO3- uptake rates were higher than NH4+ uptake rates. Riverine N loading was sometimes insufficient to meet assimilatory and dissimilatory demands, but N fixation alleviated this deficit. N fixation made up 23.7–85.4 % of total phytoplankton N acquisition and indirectly supports Planktothrix blooms. However, N fixation rates were surprisingly uncorrelated with NO3- or NH4+ concentrations. Owing to temporal separation in sources and sinks of N to Lake Erie, Sandusky Bay oscillates between a conduit and a filter of downstream N loading to Lake Erie, delivering extensively recycled forms of N during periods of low export. Drowned river mouths such as Sandusky Bay are mediators of downstream N loading, but climate-change-induced increases in precipitation and N loading will likely intensify N export from these systems.

scholarly journals Phytoplankton community composition in the south-eastern Black Sea determined with pigments measured by HPLC-CHEMTAX analyses and microscopy cell counts

2014 ◽  
Vol 95 (1) ◽  
pp. 35-52 ◽  
Author(s):  
Ertugrul Agirbas ◽  
Ali Muzaffer Feyzioglu ◽  
Ulgen Kopuz ◽  
Carole A. Llewellyn
Keyword(s):  
Black Sea ◽  
High Performance ◽  
Phytoplankton Community ◽  
Phytoplankton Community Structure ◽  
The South ◽  
Coastal Station ◽  
Chl A ◽  
South Eastern ◽  
Cell Counts ◽  
Offshore Station

The phytoplankton community structure and abundance in the south-eastern Black Sea was measured from February to December 2009 using and comparing high performance liquid chromatography pigment and microscopy analyses. The phytoplankton community was characterized by diatoms, dinoflagellates and coccolithophores, as revealed by both techniques. Fucoxanthin, diadinoxanthin, peridinin and 19′-hexanoyloxyfucoxanthin were the main accessory pigments showing significant correlation with diatom-C r2 = 0.56–0.71, P < 0.05), diatom-C (r2 = 0.85–0.91, P < 0.001), dinoflagellate-C (r2 = 0.39–0.88, P < 0.05) and coccolithophore-C (r2 = 0.80–0.71, P < 0.05), respectively. Microscopy counts indicated a total of 89 species, 71% of which were dinoflagellates, 23% were diatoms and 6% other species (mainly coccolithophores). Pigment-CHEMTAX analysis also indicated the presence of pico- and nanoplankton. Phytoplankton carbon (phyto-C) concentrations were highest in the upper water column, whereas chlorophyll-a (Chl-a) showed a deep maximum. Average phyto-C was higher at the coastal station (291 ± 66 µg l−1) than at the offshore station (258 ± 35 µg l−1), not statistically different (P > 0.05). The coastal station also had higher Chl-a concentrations (0.52–3.83 µg l−1) compared to the offshore station (0.63–2.55 µg l−1), not significant (P > 0.05). Our results are consistent with other studies and indicate that the southern Black Sea is shifting towards mesotrophy with the increasing prevalence of dinoflagellates compared to diatoms.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

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