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 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 Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans

2019 ◽  
Vol 11 (22) ◽  
pp. 2609 ◽  
Author(s):  
Stephanie Clay ◽  
Angelica Peña ◽  
Brendan DeTracey ◽  
Emmanuel Devred
Keyword(s):  
Chlorophyll A ◽  
Science Studies ◽  
Infrared Imaging ◽  
Seasonal Pattern ◽  
Wide Field ◽  
Ocean Colour ◽  
Band Ratio ◽  
Chlorophyll A Concentration ◽  
Ocean Science ◽  
Moderate Resolution Imaging Spectroradiometer

Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms.

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 Factors challenging our ability to detect long-term trends in ocean chlorophyll

2013 ◽  
Vol 10 (4) ◽  
pp. 2711-2724 ◽  
Author(s):  
C. Beaulieu ◽  
S. A. Henson ◽  
Jorge L. Sarmiento ◽  
J. P. Dunne ◽  
S. C. Doney ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Global Climate ◽  
Ocean Color ◽  
Global Scale ◽  
Trend Detection ◽  
Wide Field ◽  
Detection And Attribution

Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean and Land Colour Instrument (OLCI) expected in 2014 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.

scholarly journals Short-Term Response of Chlorophyll a Concentration Due to Intense Wind and Freshwater Peak Episodes in Estuaries: The Case of Fangar Bay (Ebro Delta)

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 701
Author(s):  
Marta F-Pedrera Balsells ◽  
Manel Grifoll ◽  
Margarita Fernández-Tejedor ◽  
Manuel Espino
Keyword(s):  
Water Column ◽  
Chlorophyll A ◽  
Temporal Variability ◽  
Ebro Delta ◽  
Small Scale ◽  
Local Wind ◽  
Short Term ◽  
Chlorophyll A Concentration ◽  
Chl A ◽  
Coastal Bays

Estuaries and coastal bays are areas of large spatio-temporal variability in physical and biological variables due to environmental factors such as local wind, light availability, freshwater inputs or tides. This study focuses on the effect of strong wind events and freshwater peaks on short-term chlorophyll a (Chl a) concentration distribution in the small-scale and microtidal, Fangar Bay (Ebro Delta, northwestern Mediterranean). The hydrodynamics of this bay are primarily driven by local wind episodes modulated by stratification in the water column. Results based on field-campaign observations and Sentinel-2 images revealed that intense wind episodes from both NW (offshore) and NE-E (onshore) caused an increase in the concentration of surface Chl a. The mechanisms responsible were horizontal mixing and the bottom resuspension (also linked to the breakage of the stratification) that presumably resuspended Chl a containing biomass (i.e., micropyhtobentos) and/or incorporated nutrients into the water column. On the other hand, sea-breeze was not capable of breaking up the stratification, so the chlorophyll a concentration did not change significantly during these episodes. It was concluded that the mixing produced by the strong winds favoured an accumulation of Chl a concentration, while the stratification that causes a positive estuarine circulation reduced this accumulation. However, the spatial-temporal variability of the Chl a concentration in small-scale estuaries and coastal bays is quite complex due to the many factors involved and deserve further intensive field campaigns and additional numerical modelling efforts.

scholarly journals Pengaruh Sebaran Konsentrasi Klorofil-a Berdasarkan Citra Satelit terhadap Hasil Tangkapan Ikan Tongkol (Euthynnus sp) Di Perairan Selat Bali

2017 ◽  
Vol 3 (1) ◽  
pp. 30 ◽  
Author(s):  
I Made Satya Prayoga ◽  
I Dewa Nyoman Nurweda Putra ◽  
I Gusti Ngurah Putra Dirgayusa
Keyword(s):  
Time Series ◽  
Correlation Coefficient ◽  
Chlorophyll A ◽  
Temporal Variability ◽  
Concentration Distribution ◽  
Cross Correlation ◽  
Tuna Fish ◽  
Chlorophyll A Concentration ◽  
Seasonal Time Series ◽  
Chlorophyll A Distribution

One of fisheries potential in Bali Strait is tuna fish (Euthynnus sp). Tuna fish (Euthynnus sp) resources is highly influenced by waters productivity which indicated by the chlorophyll-a concentration distribution. The aims of this study are: to find out the concentration spatial of chlorophyll-a distribution in Bali strait, to find out temporal variability of chlorophyll-a and tuna fish (Euthynnus sp) in Bali strait, and to find out the influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait. The analysis of the influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait uses regression polynomial order 2, correlation, and cross correlation. The influence of chlorophyll-a concentration distribution to the catch of tuna fish (Euthynnus sp) in Bali strait yearly time series climatology amounted to R2 = 0,1624 or 16,24%, the correlation coefficient values obtained by r = 0,1889. Seasonal time series climatology in west season (December - February) R2 = 0,0707 or 7,07%, the correlation coefficient values obtained by r = 0,0749. The transitional season 1 (March - May) R2 = 0,0095 or 0,95%, the correlation coefficient values obtained by r = - 0,0092. The east season (June - August) R2 = 0,086 or 8,6%, the correlation coefficient values obtained by r = - 0,2155. The transitional season 2 (September - November) R2 = 0,0482 or 4,82%. The correlation coefficient values obtained by r = - 0,1805

scholarly journals Factors challenging our ability to detect long-term trends in ocean chlorophyll

2012 ◽  
Vol 9 (11) ◽  
pp. 16419-16456 ◽  
Author(s):  
C. Beaulieu ◽  
S. A. Henson ◽  
J. L. Sarmiento ◽  
J. P. Dunne ◽  
S. C. Doney ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Global Climate ◽  
Ocean Color ◽  
Global Scale ◽  
Trend Detection ◽  
Wide Field ◽  
Detection And Attribution

Abstract. Global climate change is expected to affect the ocean's biological productivity. The most comprehensive information available about the global distribution of contemporary ocean primary productivity is derived from satellite data. Large spatial patchiness and interannual to multidecadal variability in chlorophyll a concentration challenges efforts to distinguish a global, secular trend given satellite records which are limited in duration and continuity. The longest ocean color satellite record comes from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), which failed in December 2010. The Moderate Resolution Imaging Spectroradiometer (MODIS) ocean color sensors are beyond their originally planned operational lifetime. Successful retrieval of a quality signal from the current Visible Infrared Imager Radiometer Suite (VIIRS) instrument, or successful launch of the Ocean Land Colour Instrument (OLCI) in 2013 will hopefully extend the ocean color time series and increase the potential for detecting trends in ocean productivity in the future. Alternatively, a potential discontinuity in the time series of ocean chlorophyll a, introduced by a change of instrument without overlap and opportunity for cross-calibration, would make trend detection even more challenging. In this paper, we demonstrate that there are a few regions with statistically significant trends over the ten years of SeaWiFS data, but at a global scale the trend is not large enough to be distinguished from noise. We quantify the degree to which red noise (autocorrelation) especially challenges trend detection in these observational time series. We further demonstrate how discontinuities in the time series at various points would affect our ability to detect trends in ocean chlorophyll a. We highlight the importance of maintaining continuous, climate-quality satellite data records for climate-change detection and attribution studies.

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 Adjusting Neural Network to a Particular Problem: Neural Network-Based Empirical Biological Model for Chlorophyll Concentration in the Upper Ocean

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Vladimir Krasnopolsky ◽  
Sudhir Nadiga ◽  
Avichal Mehra ◽  
Eric Bayler
Keyword(s):  
Neural Network ◽  
Chlorophyll A ◽  
Ocean Dynamics ◽  
Sea Surface ◽  
Biological Model ◽  
Upper Ocean ◽  
Wide Field ◽  
Biological Processes ◽  
Chl A ◽  
The Impact

The versatility of the neural network (NN) technique allows it to be successfully applied in many fields of science and to a great variety of problems. For each problem or class of problems, a generic NN technique (e.g., multilayer perceptron (MLP)) usually requires some adjustments, which often are crucial for the development of a successful application. In this paper, we introduce a NN application that demonstrates the importance of such adjustments; moreover, in this case, the adjustments applied to a generic NN technique may be successfully used in many other NN applications. We introduce a NN technique, linking chlorophyll “a” (chl-a) variability—primarily driven by biological processes—with the physical processes of the upper ocean using a NN-based empirical biological model for chl-a. In this study, satellite-derived surface parameter fields, sea-surface temperature (SST) and sea-surface height (SSH), as well as gridded salinity and temperature profiles from 0 to 75m depth are employed as signatures of upper-ocean dynamics. Chlorophyll-a fields from NOAA’s operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as Moderate Resolution Imaging Spectroradiometer (MODIS) and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) chl-a concentrations. Different methods of optimizing the NN technique are investigated. Results are assessed using the root-mean-square error (RMSE) metric and cross-correlations between observed ocean color (OC) fields and NN output. To reduce the impact of noise in the data and to obtain a stable computation of the NN Jacobian, an ensemble of NN with different weights is constructed. This study demonstrates that the NN technique provides an accurate, computationally cheap method to generate long (up to 10 years) time series of consistent chl-a concentration that are in good agreement with chl-a data observed by different satellite sensors during the relevant period. The presented NN demonstrates a very good ability to generalize in terms of both space and time. Consequently, the NN-based empirical biological model for chl-a can be used in oceanic models, coupled climate prediction systems, and data assimilation systems to dynamically consider biological processes in the upper ocean.

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