Assessment of Chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data

Abstract The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated (r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN.

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Satellite and In Situ Monitoring of Chl-a, Turbidity, and Total Suspended Matter in Coastal Waters: Experience of the Year 2017 along the French Coasts

Journal of Marine Science and Engineering ◽

10.3390/jmse8090665 ◽

2020 ◽

Vol 8 (9) ◽

pp. 665

Author(s):

Francis Gohin ◽

Philippe Bryère ◽

Alain Lefebvre ◽

Pierre-Guy Sauriau ◽

Nicolas Savoye ◽

...

Keyword(s):

Mediterranean Sea ◽

Water Clarity ◽

In Situ Monitoring ◽

Free Atmosphere ◽

Chl A ◽

Southern Bight ◽

In Situ Data ◽

Satellite Retrievals ◽

The Mediterranean

The consistency of satellite and in situ time series of Chlorophyll-a (Chl-a), Turbidity and Total Suspended Matters (TSM) was investigated at 17 coastal stations throughout the year 2017. These stations covered different water types, from relatively clear waters in the Mediterranean Sea to moderately turbid regions in the Bay of Biscay and the southern bight of the North-Sea. Satellite retrievals were derived from MODIS/AQUA, VIIRS/NPP and OLCI-A/Sentinel-3 spectral reflectance. In situ data were obtained from the coastal phytoplankton networks SOMLIT (CNRS), REPHY (Ifremer) and associated networks. Satellite and in situ retrievals of the year 2017 were compared to the historical seasonal cycles and percentiles, 10 and 90, observed in situ. Regarding the sampling frequency in the Mediterranean Sea, a weekly in situ sampling allowed all major peaks in Chl-a caught from space to be recorded at sea, and, conversely, all in situ peaks were observed from space in a frequently cloud-free atmosphere. In waters of the Eastern English Channel, lower levels of Chl-a were observed, both in situ and from space, compared to the historical averages. However, despite a good overall agreement for low to moderate biomass, the satellite method, based on blue and green wavelengths, tends to provide elevated and variable Chl-a in a high biomass environment. Satellite-derived TSM and Turbidity were quite consistent with in situ measurements. Moreover, satellite retrievals of the water clarity parameters often showed a lower range of variability than their in situ counterparts did, being less scattered above and under the seasonal curves of percentiles 10 and 90.

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A Prediction Model of Water In Situ Data Change under the Influence of Environmental Variables in Remote Sensing Validation

Remote Sensing ◽

10.3390/rs13010070 ◽

2020 ◽

Vol 13 (1) ◽

pp. 70

Author(s):

Futai Xie ◽

Zui Tao ◽

Xiang Zhou ◽

Tingting Lv ◽

Jin Wang ◽

...

Keyword(s):

Remote Sensing ◽

Prediction Model ◽

Environmental Variables ◽

Time Window ◽

Generalized Regression Neural Network ◽

Chl A ◽

Data Matching ◽

In Situ Data ◽

Relative Errors

Validation is an essential process to evaluate the quality of waterbody remote sensing products, and the reliability and effective application of the in situ data of waterbody parameters are an important part of validation. Based on the in situ data of chlorophyll-a (Chl-a), total suspended solids (TSS) and other environmental variables (EVs) measured at the fixed station in Taihu Lake, we attempt to develop a prediction model to determine whether the in situ measurement has enough representativeness for validating waterbody remote sensing products. Key EVs that affect the changes of Chl-a and TSS are firstly identified by using correlation analysis, which participate in modeling as variables. In addition, three multi-parameter modeling approaches are selected to simulate the daily changes of Chl-a and TSS under different EVs configurations. The results indicate that the highest prediction accuracy can be achieved through the generalized regression neural network (GRNN) based model. In the all-valid dataset, the testing absolute average relative errors (AEs) of GRNN-based Chl-a and TSS prediction model are 11.4% and 11.3%, respectively, and in the sunny-day dataset, the testing AEs are 8.6% and 8.2%, respectively. Meanwhile, the application example proves that the prediction model in this paper can be effectively used to screen the in situ data and determine the time window for satellite-ground data matching.

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Inferring particle dynamics in the Mediterranean Sea from in-situ pump POC and chloropigment data using Bayesian statistics

10.5194/bg-2018-6 ◽

2018 ◽

Cited By ~ 3

Author(s):

Weilei Wang ◽

Cindy Lee ◽

Francois Primeau

Keyword(s):

Mediterranean Sea ◽

Rate Constants ◽

Particle Aggregation ◽

Inversion Method ◽

Particle Dynamic ◽

Concentration Data ◽

Data Set ◽

Aggregation And Disaggregation ◽

The Mediterranean

Abstract. Chloropigment and particulate organic carbon (POC) concentration data collected using in-situ large-volume pumps during the MedFlux project in the Mediterranean Sea in May 2005 provided an opportunity to estimate rate constants that control the fate of particles and specifically chloropigments in the water column. Additionally, comparisons to thorium and chloropigment data from settling-velocity (SV) sediment traps at the same site enabled us to distinguish between the influence of the sampling method used vs. the tracer used on particle dynamic rate constants. Here we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and phaeopigment degradation, and particle aggregation and disaggregation. The estimated small-particle (1–70 μm) POC respiration rate constant was 1.25+0.55−0.38 yr−1 (0.80 yr). For this data set, the rate constants for chlorophyll (Chl) degradation to phaeopigments and phaeopigment respiration were not well constrained. The estimated aggregation and disaggregation rate constants were 7.65+3.35−2.33 (0.13 yr) and 106.09+39.13−28.59 yr−1 (0.01 yr), respectively, which indicates that particle aggregation and disaggregation were extensive at the studied depths (125–750 m) in May after the spring bloom had ended and flux was low.

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Towards a merged satellite and in situ fluorescence ocean chlorophyll product

Biogeosciences ◽

10.5194/bg-9-2111-2012 ◽

2012 ◽

Vol 9 (6) ◽

pp. 2111-2125 ◽

Cited By ~ 27

Author(s):

H. Lavigne ◽

F. D'Ortenzio ◽

H. Claustre ◽

A. Poteau

Keyword(s):

Chlorophyll A ◽

Correction Coefficient ◽

Data Set ◽

Chlorophyll A Concentration ◽

Profiling Float ◽

In Situ Data ◽

Biogeochemical Models ◽

Wide Range ◽

Similar Accuracy

Abstract. Understanding the ocean carbon cycle requires a precise assessment of phytoplankton biomass in the oceans. In terms of numbers of observations, satellite data represent the largest available data set. However, as they are limited to surface waters, they have to be merged with in situ observations. Amongst the in situ data, fluorescence profiles constitute the greatest data set available, because fluorometers have operated routinely on oceanographic cruises since the 1970s. Nevertheless, fluorescence is only a proxy of the total chlorophyll a concentration and a data calibration is required. Calibration issues are, however, sources of uncertainty, and they have prevented a systematic and wide range exploitation of the fluorescence data set. In particular, very few attempts to standardize the fluorescence databases have been made. Consequently, merged estimations with other data sources (e.g. satellite) are lacking. We propose a merging method to fill this gap. It consists firstly in adjusting the fluorescence profile to impose a zero chlorophyll a concentration at depth. Secondly, each point of the fluorescence profile is then multiplied by a correction coefficient, which forces the chlorophyll a integrated content measured on the fluorescence profile to be consistent with the concomitant ocean colour observation. The method is close to the approach proposed by Boss et al. (2008) to correct fluorescence data of a profiling float, although important differences do exist. To develop and test our approach, in situ data from three open ocean stations (BATS, HOT and DYFAMED) were used. Comparison of the so-called "satellite-corrected" fluorescence profiles with concomitant bottle-derived estimations of chlorophyll a concentration was performed to evaluate the final error (estimated at 31%). Comparison with the Boss et al. (2008) method, using a subset of the DYFAMED data set, demonstrated that the methods have similar accuracy. The method was applied to two different data sets to demonstrate its utility. Using fluorescence profiles at BATS, we show that the integration of "satellite-corrected" fluorescence profiles in chlorophyll a climatologies could improve both the statistical relevance of chlorophyll a averages and the vertical structure of the chlorophyll a field. We also show that our method could be efficiently used to process, within near-real time, profiles obtained by a fluorometer deployed on autonomous platforms, in our case a bio-optical profiling float. The application of the proposed method should provide a first step towards the generation of a merged satellite/fluorescence chlorophyll a product, as the "satellite-corrected" profiles should then be consistent with satellite observations. Improved climatologies with more consistent satellite and in situ data are likely to enhance the performance of present biogeochemical models.

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Computation of a new mean dynamic topography for the Mediterranean Sea from model outputs, altimeter measurements and oceanographic in situ data

Ocean Science ◽

10.5194/os-10-731-2014 ◽

2014 ◽

Vol 10 (4) ◽

pp. 731-744 ◽

Cited By ~ 49

Author(s):

M.-H. Rio ◽

A. Pascual ◽

P.-M. Poulain ◽

M. Menna ◽

B. Barceló ◽

...

Keyword(s):

Mediterranean Sea ◽

External Validation ◽

Data Sets ◽

Dynamic Topography ◽

Data Set ◽

Mean Dynamic Topography ◽

In Situ Data ◽

Velocity Variance ◽

Sensitivity Studies ◽

The Mediterranean

Abstract. The accurate knowledge of the ocean's mean dynamic topography (MDT) is a crucial issue for a number of oceanographic applications and, in some areas of the Mediterranean Sea, important limitations have been found pointing to the need of an upgrade. We present a new MDT that was computed for the Mediterranean Sea. It profits from improvements made possible by the use of extended data sets and refined processing. The updated data set spans the 1993–2012 period and consists of drifter velocities, altimetry data, hydrological profiles and model data. The methodology is similar to the previous MDT by Rio et al. (2007). However, in Rio et al. (2007) no hydrological profiles had been taken into account. This required the development of dedicated processing. A number of sensitivity studies have been carried out to obtain the most accurate MDT as possible. The main results from these sensitivity studies are the following: moderate impact to the choice of correlation scales but almost negligible sensitivity to the choice of the first guess (model solution). A systematic external validation to independent data has been made to evaluate the performance of the new MDT. Compared to previous versions, SMDT-MED-2014 (Synthetic Mean Dynamic Topography of the MEDiterranean sea) features shorter-scale structures, which results in an altimeter velocity variance closer to the observed velocity variance and, at the same time, gives better Taylor skills.

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An oceanographic survey for oil spill monitoring and model forecasting validation using remote sensing and in situ data in the Mediterranean Sea

Deep Sea Research Part II Topical Studies in Oceanography ◽

10.1016/j.dsr2.2016.02.013 ◽

2016 ◽

Vol 133 ◽

pp. 132-145 ◽

Cited By ~ 22

Author(s):

A. Pisano ◽

M. De Dominicis ◽

W. Biamino ◽

F. Bignami ◽

S. Gherardi ◽

...

Keyword(s):

Remote Sensing ◽

Mediterranean Sea ◽

Oil Spill ◽

In Situ Data ◽

Océanographic Survey ◽

The Mediterranean

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An Artificial Neural Network to Infer the Mediterranean 3D Chlorophyll-a and Temperature Fields from Remote Sensing Observations

Remote Sensing ◽

10.3390/rs12244123 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4123

Author(s):

Michela Sammartino ◽

Bruno Buongiorno Nardelli ◽

Salvatore Marullo ◽

Rosalia Santoleri

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Temperature Fields ◽

Spatial And Temporal Variability ◽

Dynamic Topography ◽

In Situ Data ◽

Artificial Neural ◽

The Mediterranean ◽

3D Fields

Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.

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Mapping Chlorophyll-a Concentrations in the Kaštela Bay and Brač Channel Using Ridge Regression and Sentinel-2 Satellite Images

Electronics ◽

10.3390/electronics10233004 ◽

2021 ◽

Vol 10 (23) ◽

pp. 3004

Author(s):

Antonia Ivanda ◽

Ljiljana Šerić ◽

Marin Bugarić ◽

Maja Braović

Keyword(s):

Remote Sensing ◽

Chlorophyll A ◽

Satellite Data ◽

Coastal Waters ◽

Optical Parameter ◽

Data Set ◽

Chl A ◽

Sentinel 2

In this paper, we describe a method for the prediction of concentration of chlorophyll-a (Chl-a) from satellite data in the coastal waters of Kaštela Bay and the Brač Channel (our case study areas) in the Republic of Croatia. Chl-a is one of the parameters that indicates water quality and that can be measured by in situ measurements or approximated as an optical parameter with remote sensing. Remote sensing products for monitoring Chl-a are mostly based on the ocean and open sea monitoring and are not accurate for coastal waters. In this paper, we propose a method for remote sensing monitoring that is locally tailored to suit the focused area. This method is based on a data set constructed by merging Sentinel 2 Level-2A satellite data with in situ Chl-a measurements. We augmented the data set horizontally by transforming the original feature set, and vertically by adding synthesized zero measurements for locations without Chl-a. By transforming features, we were able to achieve a sophisticated model that predicts Chl-a from combinations of features representing transformed bands. Multiple Linear Regression equation was derived to calculate Chl-a concentration and evaluated quantitatively and qualitatively. Quantitative evaluation resulted in R2 scores 0.685 and 0.659 for train and test part of data set, respectively. A map of Chl-a of the case study area was generated with our model for the dates of the known incidents of algae blooms. The results that we obtained are discussed in this paper.

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Towards a merged satellite and in situ fluorescence ocean chlorophyll product

Biogeosciences Discussions ◽

10.5194/bgd-8-11899-2011 ◽

2011 ◽

Vol 8 (6) ◽

pp. 11899-11939

Author(s):

H. Lavigne ◽

F. D'Ortenzio ◽

H. Claustre ◽

A. Poteau

Keyword(s):

Chlorophyll A ◽

Correction Coefficient ◽

Data Set ◽

Chlorophyll A Concentration ◽

Profiling Float ◽

In Situ Data ◽

Biogeochemical Models ◽

Wide Range ◽

Similar Accuracy

Abstract. Understanding the ocean carbon cycle requires a precise assessment of phytoplankton biomass in the oceans. In terms of numbers of observations, satellite data represents the largest available data set. However, as they are limited to surface waters, they have to be merged with in situ observations. Amongst the in situ data, fluorescence profiles constitute the greatest data set available, because fluorometers operate routinely on oceanographic cruise since the seventies. Nevertheless, fluorescence is only a proxy of the Total Chlorophyll-a concentration and a data calibration is required. Calibration issues are, however, source of uncertainty and they have prevented a systematic and wide range exploitation of the fluorescence data set. In particular, very few attempts to standardize the fluorescence data bases exist. Consequently, merged estimations with other data sources (i.e. satellite) are lacking. We propose a merging method to fill this gap. It consists firstly, in adjusting the fluorescence profile to impose a zero Chlorophyll-a concentration at depth. Secondly, each point of the fluorescence profile is then multiplied by a correction coefficient which forces the Chlorophyll-a integrated content measured on the fluorescence profile to be consistent with the concomitant ocean color observation. The method is close to the approach proposed by Boss et al. (2008) to calibrate fluorescence data of a profiling float, although important differences do exist. To develop and test our approach, in situ data from three open ocean stations (BATS, HOT and DYFAMED) were used. Comparison of the so-called "satellite-corrected" fluorescence profiles with concomitant bottle derived estimations of Chlorophyll-a concentration was performed to evaluate the final error, which resulted to be of about 31 %. Comparison with the Boss et al. (2008) method, carried out on a subset of the DYFAMED data set simulating a profiling float time series, demonstrated that the methods have similar accuracy. Applications of the method were then explored on two different data sets. Using fluorescence profiles at BATS, we show that the integration of "satellite-corrected" fluorescence profiles in Chlorophyll-a climatologies could improve both the statistical relevance of Chlorophyll-a averages and the vertical structure of the Chlorophyll-a field. We also show that our method could be efficiently used to process, within near-real time, profiles obtained by a fluorometer deployed on autonomous platforms, in our case a bio-optical profiling float. The wide application of the proposed method should provide a first step toward the generation of a merged satellite/fluorescence Chlorophyll-a product, as the "satellite-corrected" profiles should then be consistent with satellite observations. Improved climatologies and more consistent satellite and in situ data (comprising those from autonomous platforms) should strongly enhance the performance of present biogeochemical models.

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