scholarly journals Seasonal Cycles of Phytoplankton Expressed by Sine Equations Using the Daily Climatology from Satellite-Retrieved Chlorophyll-a Concentration (1997–2019) Over Global Ocean

2020 ◽  
Vol 12 (16) ◽  
pp. 2662 ◽  
Author(s):  
Zexi Mao ◽  
Zhihua Mao ◽  
Cédric Jamet ◽  
Marc Linderman ◽  
Yuntao Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Seasonal Variations ◽  
Global Ocean ◽  
Boreal Winter ◽  
Seasonal Cycles ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Global Oceans ◽  
Sine Equation

The global coverage of Chlorophyll-a concentration (Chl-a) has been continuously available from ocean color satellite sensors since September 1997 and the Chl-a data (1997–2019) were used to produce a climatological dataset by averaging Chl-a values at same locations and same day of year. The constructed climatology can remarkably reduce the variability of satellite data and clearly exhibit the seasonal cycles, demonstrating that the growth and decay of phytoplankton recurs with similarly seasonal cycles year after year. As the shapes of time series of the climatology exhibit strong periodical change, we wonder whether the seasonality of Chl-a can be expressed by a mathematic equation. Our results show that sinusoid functions are suitable to describe cyclical variations of data in time series and patterns of the daily climatology can be matched by sine equations with parameters of mean, amplitude, phase, and frequency. Three types of sine equations were used to match the climatological Chl-a with Mean Relative Differences (MRD) of 7.1%, 4.5%, and 3.3%, respectively. The sine equation with four sinusoids can modulate the shapes of the fitted values to match various patterns of climatology with small MRD values (less than 5%) in about 90% of global oceans. The fitted values can reflect an overall pattern of seasonal cycles of Chl-a which can be taken as a time series of biomass baseline for describing the state of seasonal variations of phytoplankton. The amplitude images, the spatial patterns of seasonal variations of phytoplankton, can be used to identify the transition zone chlorophyll fronts. The timing of phytoplankton blooms is identified by the biggest peak of the fitted values and used to classify oceans as different bloom seasons, indicating that blooms occur in all four seasons with regional features. In global oceans within latitude domains (48°N–48°S), blooms occupy approximately half of the ocean (50.6%) during boreal winter (December–February) in the northern hemisphere and more than half (58.0%) during austral winter (June–August) in the southern hemisphere. Therefore, the sine equation can be used to match the daily Chl-a climatology and the fitted values can reflect the seasonal cycles of phytoplankton, which can be used to investigate the underlying phenological characteristics.

scholarly journals Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration

2009 ◽  
Vol 66 (7) ◽  
pp. 1547-1556 ◽  
Author(s):  
V. Vantrepotte ◽  
F. Mélin
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Temporal Variability ◽  
Global Scale ◽  
Wide Field ◽  
Seasonal Component ◽  
Ocean Colour ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
The Stability

Abstract Vantrepotte, V., and Mélin, F. 2009. Temporal variability of 10-year global SeaWiFS time-series of phytoplankton chlorophyll a concentration. – ICES Journal of Marine Science, 66: 1547–1556. The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) global dataset now offers a 10-year time-series of a consistent, well-calibrated, ocean colour record suitable to analyse temporal variability. The relative importance of the seasonal term in the chlorophyll a (Chl a) concentration signal is first assessed using statistical techniques of temporal decomposition. The emphasis is on the Census method II (X-11) approach, which allows year-to-year variations in the seasonal component. The seasonality detected in the SeaWiFS Chl a record is analysed through a generic province-based classification of marine ecosystems and at global scale and is found very variable spatially. Working with 5′-resolution gridded Chl a products, the contribution of the seasonal component derived from X-11 amounts to 64% of the total variance, compared with only 36% if a fixed annual cycle is assumed. The capacity of X-11 to capture interannual variations in seasonality is used to diagnose the stability of the Chl a seasonal cycle. Finally, linear changes in Chl a concentration observed after a decade of continuous ocean colour record agree globally with previous observations on shorter series. Significant changes of both signs are detected in various regions of the world’s oceans, but primarily a general decrease of Chl a in the mid-ocean gyres.

scholarly journals Enhanced chlorophyll-a concentration in the wake of Sable Island, eastern Canada, revealed by two decades of satellite observations: a response to grey seal population dynamics?

2021 ◽  
Author(s):  
Emmanuel Devred ◽  
Andrea Hilborn ◽  
Cornelia den Heyer
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Satellite Observations ◽  
Eastern Canada ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Seal Population ◽  
Good Agreement

Abstract. Elevated surface chlorophyll-a concentration, an index of phytoplankton biomass, has been previously observed and documented by remote sensing in the waters to the southwest of Sable Island (SI) on the Scotian Shelf in eastern Canada. Here, we present a detailed analysis of this phenomenon using a 20-year time series of satellite-derived chlorophyll-a concentration (chl-a), paired with information on the particle backscattering coefficient at 443 nm (bbp(443)) and the detritus/gelbstoff absorption coefficient at 443 nm (adg(443) ) in an attempt to explain the possible mechanisms that lead to the increase in surface biomass in the surroundings of SI. We compared the seasonal cycle, climatology and trends of surface waters near SI to two control regions located both upstream and downstream of the island, away from terrigenous inputs. Application of the self-organizing maps approach (SOMs) to the time series of satellite-derived chl-a over the Scotian Shelf revealed the annual spatio-temporal patterns around SI and, in particular, persistently high phytoplankton biomass during winter and spring in the leeward side of SI, a phenomenon that is not observed in the control boxes. Time series analysis of the satellite archive evidenced a long-term increase in chl-a and adg(443), and a long-term decrease in bbp(443) in all regions. In the close vicinity of SI, the increase of chl-a and adg(443) during the winter months occurred at a rate twice that of the ones observed in the control boxes. In addition to the increase of the chl-a and adg(443) within the plume southward of SI, the surface area of the plume itself has also expanded by a factor of five over the last 20 years. While the island mass effect (IME) is certainly contributing to the enhanced biomass around SI, we hypothesize that the large increase in chl-a over the last 20 years is due to an injection of nutrients by the island’s grey seal colony, which has increased by about 300 % over the last twenty years. The contribution of nutrients from seals may sustain high phytoplankton biomass at a time of year when it is usually low. A conceptual model was developed to describe the annual variation of seal abundance on SI and estimate the standing stock of chl-a concentration that can be sustained by the release of nitrogen. Comparison between satellite observations and model simulations showed a very good agreement between the seal population increase on SI during the breeding season and the phytoplankton biomass increase during the winter. In addition, the 20-year satellite-derived trend in chlorophyll-a concentration showed a good agreement with the increasing trend in seal population on SI during the same time period. The satellite data analysis supports the concept of top-down control of marine mammals over lower trophic levels through a fertilisation mechanism, although these results could not be confirmed without in situ measurements for ground truthing. Our findings challenge the idea that the IME is restricted to islands with strong bathymetric slope located in oligotrophic waters of mid-latitudes and tropics, and demonstrate that enhanced marine production can occur in other oceanic regions, with potentially substantial implications for conservation and fisheries.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

scholarly journals Assessing the potential of integrating Landsat sensors for estimating chlorophyll-a concentration in a reservoir

2017 ◽  
Vol 49 (5) ◽  
pp. 1608-1617 ◽  
Author(s):  
Matias Bonansea ◽  
Claudia Rodriguez ◽  
Lucio Pinotti
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Landsat Imagery ◽  
Quality Parameters ◽  
Landsat 8 ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Sensing Applications ◽  
Wide Range ◽  
Quality Assessments

Abstract Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors with open access will increase with time, we expect that this trend will certainly further promote remote sensing applications and serve as a valuable basis for a wide range of water quality assessments.

scholarly journals Vertical distribution of chlorophyll <I>a</I> concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean

2015 ◽  
Vol 7 (2) ◽  
pp. 261-273 ◽  
Author(s):  
R. Sauzède ◽  
H. Lavigne ◽  
H. Claustre ◽  
J. Uitz ◽  
C. Schmechtig ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Community Composition ◽  
Phytoplankton Biomass ◽  
Phytoplankton Community ◽  
Environmental Data ◽  
Global Ocean ◽  
Control Procedure ◽  
Chlorophyll A Concentration ◽  
Phytoplankton Community Composition

Abstract. In vivo chlorophyll a fluorescence is a proxy of chlorophyll a concentration, and is one of the most frequently measured biogeochemical properties in the ocean. Thousands of profiles are available from historical databases and the integration of fluorescence sensors to autonomous platforms has led to a significant increase of chlorophyll fluorescence profile acquisition. To our knowledge, this important source of environmental data has not yet been included in global analyses. A total of 268 127 chlorophyll fluorescence profiles from several databases as well as published and unpublished individual sources were compiled. Following a robust quality control procedure detailed in the present paper, about 49 000 chlorophyll fluorescence profiles were converted into phytoplankton biomass (i.e., chlorophyll a concentration) and size-based community composition (i.e., microphytoplankton, nanophytoplankton and picophytoplankton), using a method specifically developed to harmonize fluorescence profiles from diverse sources. The data span over 5 decades from 1958 to 2015, including observations from all major oceanic basins and all seasons, and depths ranging from the surface to a median maximum sampling depth of around 700 m. Global maps of chlorophyll a concentration and phytoplankton community composition are presented here for the first time. Monthly climatologies were computed for three of Longhurst's ecological provinces in order to exemplify the potential use of the data product. Original data sets (raw fluorescence profiles) as well as calibrated profiles of phytoplankton biomass and community composition are available on open access at PANGAEA, Data Publisher for Earth and Environmental Science. Raw fluorescence profiles: http://doi.pangaea.de/10.1594/PANGAEA.844212 and Phytoplankton biomass and community composition: http://doi.pangaea.de/10.1594/PANGAEA.844485

scholarly journals THE FLUCTUATION OF CHLOROPHYLL-A CONCENTRATION DERIVED FROM SATELLITE IMAGERY AND CATCH OF OILY SARDINE (SARDINELLA LEMURU0)IN BALI STRAIT

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
J. LUMBAN GAOL ◽  
WUDIANTO ◽  
B. P. PASARIBU ◽  
D. MANURUNG ◽  
R. ENDRIANI
Keyword(s):  
Chlorophyll A ◽  
Satellite Imagery ◽  
Global Climate ◽  
Time Lag ◽  
Wide Field ◽  
Positive Anomaly ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Acoustic Data ◽  
The Relationship

The investigation is aimed to know the relationship between chlorophyll-a (chl-a) concentration and the abundance of Oily sardine (Sardinella lemuru), in Bali Strait. A time series of monthly mean chl-a data derived from Ocean Color Thermal Scanner (OCTS) sensor and Sea-viewing Wide Field-of View Sensor (SeaWiFS) during 1997-1999 are used in this study. Monthly Sardinella lemuru catch during 1997-1999 are obtained from fish landing data. The abundance of Sardinella lemuru is determined from acoustic data conducted in Bali Strait in September 1998 and May 1999. The result shows that the fluctuation of chlorophyll-a concentration in Bali Strait is influenced by monsoon and global climate change phenomena such as Dipole Mode (DM) event. During southeast Monsoon the upwelling process occurred around Bali Strait, so that the chl-a concentration is increased and during DM event occurred positive anomaly of chl-a concentration. The catch of Sardinella lemuru in Bali Strait is fluctuated during 1997-1999. The correlation between chl-a concentration and lemuru catch is positive and significant with certain time lag. Key words: Chlorophyll-a, Sardinella lemuru, Bali Strait, Satellite imagery

scholarly journals Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

2020 ◽  
Vol 12 (24) ◽  
pp. 4156
Author(s):  
Elodie Martinez ◽  
Anouar Brini ◽  
Thomas Gorgues ◽  
Lucas Drumetz ◽  
Joana Roussillon ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Learning Algorithms ◽  
Global Scale ◽  
Machine Learning Algorithms ◽  
Global Ocean ◽  
Time Variability ◽  
Support Vector ◽  
Chl A ◽  
Phytoplankton Variability

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP outperforms the SVR to capture satellite Chl (correlation of 0.6 vs. 0.17 on a global scale, respectively) along with its seasonal and interannual variability, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Multi-sensor satellite time series of optical properties and chlorophyll-a concentration in the Adriatic Sea

2011 ◽  
Vol 91 (3) ◽  
pp. 229-244 ◽  
Author(s):  
F. Mélin ◽  
V. Vantrepotte ◽  
M. Clerici ◽  
D. D’Alimonte ◽  
G. Zibordi ◽  
...  
Keyword(s):  
Time Series ◽  
Optical Properties ◽  
Chlorophyll A ◽  
Adriatic Sea ◽  
Chlorophyll A Concentration

scholarly journals Comparison of In-Situ Chlorophyll-a Time Series and Sentinel-3 Ocean and Land Color Instrument Data in Slovenian National Waters (Gulf of Trieste, Adriatic Sea)

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1903
Author(s):  
El Khalil Cherif ◽  
Patricija Mozetič ◽  
Janja Francé ◽  
Vesna Flander-Putrle ◽  
Jana Faganeli-Pucer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Adriatic Sea ◽  
Global Ocean ◽  
Learning Approach ◽  
Chl A ◽  
Gulf Of Trieste ◽  
Machine Learning Approach

While satellite remote sensing of ocean color is a viable tool for estimating large-scale patterns of chlorophyll-a (Chl-a) and global ocean primary production, its application in coastal waters is limited by the complex optical properties. An exploratory study was conducted in the Gulf of Trieste (Adriatic Sea) to assess the usefulness of Sentinel-3 satellite data in the Slovenian national waters. OLCI (Ocean and Land Colour Instrument) Chl-a level 2 products (OC4Me and NN) were compared to monthly Chl-a in-situ measurements at fixed sites from 2017 to 2019. In addition, eight other methods for estimating Chl-a concentration based on reflectance in different spectral bands were tested (OC3M, OC4E, MedOC4, ADOC4, AD4, 3B-OLCI, 2B-OLCI and G2B). For some of these methods, calibration was performed on in-situ data to achieve a better agreement. Finally, L1-regularized regression and random forest were trained on the available dataset to test the capabilities of the machine learning approach. The results show rather poor performance of the two originally available products. The same is true for the other eight methods and the fits to the measured values also show only marginal improvement. The best results are obtained with the blue-green methods (OC3, OC4 and AD4), especially the AD4SI (a designated fit of AD4) with R = 0.56 and RMSE = 0.4 mg/m³, while the near infrared (NIR) methods show underwhelming performance. The machine learning approach can only explain 30% of the variability and the RMSE is of the same order as for the blue-green methods. We conclude that due to the low Chl-a concentration and the moderate turbidity of the seawater, the reflectance provided by the Sentinel-3 OLCI spectrometer carries little information about Chl-a in the Slovenian national waters within the Gulf of Trieste and is therefore of limited use for our purposes. This requires that we continue to improve satellite products for use in those marine waters that have not yet proven suitable. In this way, satellite data could be effectively integrated into a comprehensive network that would allow a reliable assessment of ecological status, taking into account environmental regulations.

scholarly journals Spatial and temporal variability of surface chlorophyll-a in the Gulf of Tomini, Sulawesi, Indonesia

2018 ◽  
Vol 19 (3) ◽  
pp. 793-801 ◽  
Author(s):  
QURNIA WULAN SARI ◽  
EKO SISWANTO ◽  
DEDI SETIABUDIDAYA ◽  
INDRA YUSTIAN ◽  
ISKHAQ ISKANDAR
Keyword(s):  
Chlorophyll A ◽  
El Niño ◽  
Temporal Variability ◽  
El Nino ◽  
Southern Oscillation ◽  
Surface Wind ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Spatial And Temporal Variability ◽  
Chl A

Sari QW, Siswanto E, Setiabudidaya D, Yustian I, Iskandar I. 2018. Spatial and temporal variability of surface chlorophyll-a in the Gulf of Tomini, Sulawesi, Indonesia. Biodiversitas 19: 793-801. The Gulf of Tomini (GoT) is mostly influenced by seasonal and interannual events. So, the immensive aim of this study is to explore spatial and temporal variations of chlorophyll-a (chl-a) and oceanographic parameters in the GoT under the influences of monsoonal winds, El Niño Southern Oscillation (ENSO), and Indian Ocean Dipole (IOD). The data were collected from the satellite imaging of chl-a and sea and surface temperature (SST) as well as surface wind from the reanalysis data for a period of January 2003 to December 2015. Monthly variations of the chl-a and SST in the GoT reveal chl-a bloom in the center part to the mouth of the GoT during the southeast monsoon season (boreal summer). The chl-a concentrations were relatively higher (>0.1 mg m-3) and distributed throughout most of the areas near the Maluku Sea. The SST in the middle of the GoT was relatively lower than that near the Maluku Sea (the eastern part of the GoT). On the other hand, during the northwest monsoon (boreal winter), the chl-a concentration decreased (<0.1 mg m-3). During this season, the SST was relatively higher (28-29 °C) than that during the boreal summer (27-26 °C) and distributed uniformly. Meanwhile, on interannual timescale, the ENSO and IOD play important role in regulating chl-a distribution in the GoT. High surface chl-a concentration was observed during El Niño and/or positive IOD events. Enhanced surface chl-a concentration during El Niño and/or positive IOD events was associated with the upward Ekman pumping induced by the southeasterly wind anomalies. The situation was reversed during the Niña and/or negative IOD events.

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