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25 pages, 8854 KiB

Open AccessArticle

Modelling the Vertical Distribution of Phytoplankton Biomass in the Mediterranean Sea from Satellite Data: A Neural Network Approach

by Michela Sammartino, Salvatore Marullo, Rosalia Santoleri and Michele Scardi

Remote Sens. 2018, 10(10), 1666; https://doi.org/10.3390/rs10101666 - 21 Oct 2018

Cited by 30 | Viewed by 5778

Abstract

Knowledge of the vertical structure of the bio-chemical properties of the ocean is crucial for the estimation of primary production, phytoplankton distribution, and biological modelling. The vertical profiles of chlorophyll-a (Chla) are available via in situ measurements that are usually quite rare [...] Read more.

Knowledge of the vertical structure of the bio-chemical properties of the ocean is crucial for the estimation of primary production, phytoplankton distribution, and biological modelling. The vertical profiles of chlorophyll-a (Chla) are available via in situ measurements that are usually quite rare and not uniformly distributed in space and time. Therefore, obtaining estimates of the vertical profile of the Chla field from surface observations is a new challenge. In this study, we employed an Artificial Neural Network (ANN) to reconstruct the 3-Dimensional (3D) Chla field in the Mediterranean Sea from surface satellite estimates. This technique is able to reproduce the highly nonlinear nature of the relationship between different input variables. A large in situ dataset of temperature and Chla calibrated fluorescence profiles, covering almost all Mediterranean Sea seasonal conditions, was used for the training and test of the network. To separate sources of errors due to surface Chla and temperature satellite estimates, from errors due to the ANN itself, the method was first applied using in situ surface data and then using satellite data. In both cases, the validation against in situ observations shows comparable statistical results with respect to the training, highlighting the feasibility of applying an ANN to infer the vertical Chla field from surface in situ and satellite estimates. We also analyzed the usefulness of our approach to resolve the Chla prediction at small temporal scales (e.g., day) by comparing it with the most widely used Mediterranean climatology (MEDATLAS). The results demonstrated that, generally, our method is able to reproduce the most reliable profile of Chla from synoptical satellite observations, thus resolving finer spatial and temporal scales with respect to climatology, which can be crucial for several marine applications. We demonstrated that our 3D reconstructed Chla field could represent a valid alternative to overcome the absence or discontinuity of in situ sampling. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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17 pages, 12579 KiB

Open AccessArticle

Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index

by Ahmed Harun-Al-Rashid and Chan-Su Yang

Remote Sens. 2018, 10(9), 1478; https://doi.org/10.3390/rs10091478 - 16 Sep 2018

Cited by 8 | Viewed by 4987

Abstract

This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection [...] Read more.

This work focuses on the detection of tiny macroalgae patches in the eastern parts of the Yellow Sea (YS) using high-resolution Landsat-8 images from 2014 to 2017. In the comparison between floating algae index (FAI) and normalized difference vegetation index (NDVI) better detection by FAI was observed, but many tiny patches still remained undetected. By applying a modification on the FAI around 12% to 27% increased and correct detection of macroalgae is achieved from 35 images compared to the original. Through this method many scattered tiny patches were detected in June or July in Korea Bay and Gyeonggi Bay. Though it was a small-scale phenomenon they occurred in the similar period of macroalgal bloom occurrence in the YS. Thus, by using this modified method we could detect macroalgae in the study areas around one month earlier than the previously used Geostationary Ocean Color Imager NDVI-based detection. Later, more macroalgae patches including smaller ones occupying increased areas were detected. Thus, it seems that those macroalgae started growing locally from tiny patches rather than being transported from the western parts of the YS. Therefore, this modified FAI could be used for the precise detection of macroalgae. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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21 pages, 1302 KiB

Open AccessArticle

A 55-Year Time Series Station for Primary Production in the Adriatic Sea: Data Correction, Extraction of Photosynthesis Parameters and Regime Shifts

by Žarko Kovač, Trevor Platt, Živana Ninčević Gladan, Mira Morović, Shubha Sathyendranath, Dionysios E. Raitsos, Branka Grbec, Frano Matić and Jere Veža

Remote Sens. 2018, 10(9), 1460; https://doi.org/10.3390/rs10091460 - 12 Sep 2018

Cited by 20 | Viewed by 4675

Abstract

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 [...] Read more.

In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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Graphical abstract
Full article ">Figure 1
Percentage difference in daily production between the estimate based on linear extrapolation of average production during a six-hour interval around noon (<a href="#FD5-remotesensing-10-01460" class="html-disp-formula">5</a>) and the analytical solution for daily production (<a href="#FD6-remotesensing-10-01460" class="html-disp-formula">6</a>). The orange curve corresponds to the Julian day with the longest day length <math display="inline"><semantics> <msub> <mi>D</mi> <mi>max</mi> </msub> </semantics></math> and the blue with the shortest day length <math display="inline"><semantics> <msub> <mi>D</mi> <mi>min</mi> </msub> </semantics></math>, at the latitude of the Stončica station. Full article ">Figure 2
Histogram of estimated values of the assimilation number <math display="inline"><semantics> <msubsup> <mi>P</mi> <mrow> <mi>m</mi> </mrow> <mi>B</mi> </msubsup> </semantics></math> obtained from 185 cruises in the Adriatic Sea. The abscissa corresponds to parameter value, and the ordinate gives the percentage of cruises that fall into a certain interval of parameter values. Full article ">Figure 3
Measured versus modelled normalized production at depth. Production at depth is measured during a 6-h incubation interval and normalized to biomass measurement at the corresponding depth. Modelled production is given by numerical integration of (<a href="#FD3-remotesensing-10-01460" class="html-disp-formula">3</a>) with the recovered photosynthesis parameters. The <math display="inline"><semantics> <msup> <mi>r</mi> <mn>2</mn> </msup> </semantics></math> between the measured and the modelled normalized production at depth is 0.87. In total, there are 1040 points. Full article ">Figure 4
Comparison of water column production <math display="inline"><semantics> <msub> <mover accent="true"> <mi>P</mi> <mo>˜</mo> </mover> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math> (abscissa) calculated by (<a href="#FD9-remotesensing-10-01460" class="html-disp-formula">9</a>) from extrapolated daily production at depth and water column production <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math> (ordinate) calculated by (<a href="#FD10-remotesensing-10-01460" class="html-disp-formula">10</a>) from the analytical solution for daily production, with parameters estimated from the measured production profiles. The dashed grey line is the 1:1 line, and the thick grey line is the linear regression between <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mover accent="true"> <mi>P</mi> <mo>˜</mo> </mover> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math>. Full article ">Figure 5
Annual running means of water column production from April 1962–July 2017. The original time-series, obtained by application of (<a href="#FD9-remotesensing-10-01460" class="html-disp-formula">9</a>), is given in grey. The corrected time-series, obtained by application of (<a href="#FD10-remotesensing-10-01460" class="html-disp-formula">10</a>), is given in thick blue. Start of each regime is indicated by an orange circle with the corresponding year above it. The red line at the beginning of the sampling era highlights the period without chlorophyll measurements. Full article ">Figure 6
Time-series of measured (orange) and remotely-sensed chlorophyll (grey). Measured chlorophyll corresponds to the vertically-averaged chlorophyll concentration for each profile. The grey curve is the mean remotely-sensed chlorophyll concentration, and the shaded area corresponds to the min-max range. Data are from September 1997–December 2016. Full article ">Figure 7
(a) Seasonal cycles of remotely-sensed (green points) and fitted chlorophyll (green curve), alongside with the seasonal cycle of the assimilation number <math display="inline"><semantics> <msubsup> <mi>P</mi> <mi>m</mi> <mi>B</mi> </msubsup> </semantics></math> (thin blue curve) and the fitted <math display="inline"><semantics> <msubsup> <mi>P</mi> <mi>m</mi> <mi>B</mi> </msubsup> </semantics></math> (thick blue curve). (b) Estimated water column production <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math> using remotely-sensed chlorophyll and <math display="inline"><semantics> <msubsup> <mi>P</mi> <mi>m</mi> <mi>B</mi> </msubsup> </semantics></math> from above, with the climatological (light grey) and one half the climatological (dark grey) values for noon irradiance on each Julian day, alongside <math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>Z</mi> <mo>,</mo> <mi>T</mi> </mrow> </msub> </semantics></math> obtained by using (<a href="#FD10-remotesensing-10-01460" class="html-disp-formula">10</a>) (thin orange curve) and the fitted seasonal cycle (thick orange curve). Full article ">

21 pages, 8258 KiB

Open AccessArticle

Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region

by Andrea Hilborn and Maycira Costa

Remote Sens. 2018, 10(9), 1449; https://doi.org/10.3390/rs10091449 - 11 Sep 2018

Cited by 51 | Viewed by 7910

Abstract

A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, [...] Read more.

A major limitation for remote sensing analyses of oceanographic variables is loss of spatial data. The Data INterpolating Empirical Orthogonal Functions (DINEOF) method has demonstrated effectiveness for filling spatial gaps in remote sensing datasets, making them more easily implemented in further applications. However, the spatial and temporal coverage of the input image dataset can heavily impact the outcomes of using this method and, thus, further metrics derived from these datasets, such as phytoplankton bloom phenology. In this study, we used a three-year time series of MODIS-Aqua chlorophyll-a to evaluate the DINEOF reconstruction output accuracy corresponding to variation in the form of the input data used (i.e., daily or week composite scenes) and time series length (annual or three consecutive years) for a dynamic region, the Salish Sea, Canada. The accuracy of the output data was assessed considering the original chla pixels. Daily input time series produced higher accuracy reconstructing chla (95.08–97.08% explained variance, RMSE_xval 1.49–1.65 mg m⁻³) than did all week composite counterparts (68.99–76.88% explained variance, RMSE_xval 1.87–2.07 mg m⁻³), with longer time series producing better relationships to original chla pixel concentrations. Daily images were assessed relative to extracted in situ chla measurements, with original satellite chla achieving a better relationship to in situ matchups than DINEOF gap-filled chla, and with annual DINEOF-processed data performing better than the multiyear. These results contribute to the ongoing body of work encouraging production of ocean color datasets with consistent processing for global purposes such as climate change studies. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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17 pages, 5504 KiB

Open AccessArticle

Evaluation of Semi-Analytical Algorithms to Retrieve Particulate and Dissolved Absorption Coefficients in Gulf of California Optically Complex Waters

by Stella Patricia Betancur-Turizo, Adriana González-Silvera, Eduardo Santamaría-del-Ángel, Jing Tan and Robert Frouin

Remote Sens. 2018, 10(9), 1443; https://doi.org/10.3390/rs10091443 - 10 Sep 2018

Cited by 6 | Viewed by 3932

Abstract

Two semi-analytical algorithms, Generalized Inherent Optical Property (GIOP) and Garver-Siegel-Maritorena (GSM), were evaluated in terms of how well they reproduced the absorption coefficient of phytoplankton (a_ph(λ)) and dissolved and detrital organic matter (a_dg(λ)) [...] Read more.

Two semi-analytical algorithms, Generalized Inherent Optical Property (GIOP) and Garver-Siegel-Maritorena (GSM), were evaluated in terms of how well they reproduced the absorption coefficient of phytoplankton (a_ph(λ)) and dissolved and detrital organic matter (a_dg(λ)) at three wavelengths (λ of 412, 443, and 488 nm) in a zone with optically complex waters, the Upper Gulf of California (UGC) and the Northern Gulf of California (NGC). In the UGC, detritus determines most of the total light absorption, whereas, in the NGC, chromophoric dissolved organic material (CDOM) and phytoplankton dominate. Upon comparing the results of each model with a database assembled from four cruises done from spring to summer (March through September) between 2011 and 2013, it was found that GIOP is a better estimator for a_ph(λ) than GSM, independently of the region. However, both algorithms underestimate in situ values in the NGC, whereas they overestimate them in the UGC. Errors are associated with the following: (a) the constant a*_ph(λ) value used by GSM and GIOP (0.055 m² mgChla⁻¹) is higher than the most frequent value observed in this study’s data (0.03 m² mgChla⁻¹), and (b) satellite-derived chlorophyll a concentration (Chla) is biased high compared with in situ Chla. GIOP gave also better results for the a_dg(λ) estimation than GSM, especially in the NGC. The spectral slope S_dg was identified as an important parameter for estimating a_dg(λ), and this study’s results indicated that the use of a fixed input value in models was not adequate. The evaluation confirms the lack of generality of algorithms like GIOP and GSM, whose reflectance model is too simplified to capture expected variability. Finally, a greater monitoring effort is suggested in the study area regarding the collection of in situ reflectance data, which would allow explaining the effects that detritus and CDOM may have on the semi-analytical reflectance inversions, as well as isolating the possible influence of the atmosphere on the satellite-derived water reflectance and Chla. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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16 pages, 1271 KiB

Open AccessArticle

Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

by Nicole M. DeLuca, Benjamin F. Zaitchik and Frank C. Curriero

Remote Sens. 2018, 10(9), 1393; https://doi.org/10.3390/rs10091393 - 1 Sep 2018

Cited by 31 | Viewed by 5197

Abstract

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of [...] Read more.

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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22 pages, 5545 KiB

Open AccessArticle

Estimation of Size-Fractionated Primary Production from Satellite Ocean Colour in UK Shelf Seas

by Kieran Curran, Robert J. W. Brewin, Gavin H. Tilstone, Heather A. Bouman and Anna Hickman

Remote Sens. 2018, 10(9), 1389; https://doi.org/10.3390/rs10091389 - 31 Aug 2018

Cited by 13 | Viewed by 5434

Abstract

Satellite ocean-colour based models of size-fractionated primary production (PP) have been developed for the oceans on a global level. Uncertainties exist as to whether these models are accurate for temperate Shelf seas. In this paper, an existing ocean-colour based PP model is tuned [...] Read more.

Satellite ocean-colour based models of size-fractionated primary production (PP) have been developed for the oceans on a global level. Uncertainties exist as to whether these models are accurate for temperate Shelf seas. In this paper, an existing ocean-colour based PP model is tuned using a large in situ database of size-fractionated measurements from the Celtic Sea and Western English Channel of chlorophyll-a (Chl a) and the photosynthetic parameters, the maximum photosynthetic rate (

P_{m}^{B}

) and light limited slope (

α^{B}

). Estimates of size fractionated PP over an annual cycle in the UK shelf seas are compared with the original model that was parameterised using in situ data from the open ocean and a climatology of in situ PP from 2009 to 2015. The Shelf Sea model captured the seasonal patterns in size-fractionated PP for micro- and picophytoplankton, and generally performed better than the original open ocean model, except for nanophytoplankton PP which was over-estimated. The overestimation in PP is in part due to errors in the parameterisation of the biomass profile during summer, stratified conditions. Compared to the climatology of in situ data, the shelf sea model performed better when phytoplankton biomass was high, but overestimated PP at low Chl a. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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18 pages, 7129 KiB

Open AccessArticle

Variations in Remotely-Sensed Phytoplankton Size Structure of a Cyclonic Eddy in the Southwest Indian Ocean

by Tarron Lamont, Raymond G. Barlow and Robert J. W. Brewin

Remote Sens. 2018, 10(7), 1143; https://doi.org/10.3390/rs10071143 - 19 Jul 2018

Cited by 4 | Viewed by 6171

Abstract

Phytoplankton size classes were derived from weekly-averaged MODIS Aqua chlorophyll a data over the southwest Indian Ocean in order to assess changes in surface phytoplankton community structure within a cyclonic eddy as it propagated across the Mozambique Basin in 2013. Satellite altimetry was [...] Read more.

Phytoplankton size classes were derived from weekly-averaged MODIS Aqua chlorophyll a data over the southwest Indian Ocean in order to assess changes in surface phytoplankton community structure within a cyclonic eddy as it propagated across the Mozambique Basin in 2013. Satellite altimetry was used to identify and track the southwesterly movement of the eddy from its origin off Madagascar in mid-June until mid-October, when it eventually merged with the Agulhas Current along the east coast of South Africa. Nano- and picophytoplankton comprised most of the community in the early phase of the eddy development in June, but nanophytoplankton then dominated in austral winter (July and August). Microphytoplankton was entrained into the eddy by horizontal advection from the southern Madagascar shelf, increasing the proportion of microphytoplankton to 23% when the chlorophyll a levels reached a peak of 0.36 mg·m⁻³ in the third week of July. Chlorophyll a levels declined to <0.2 mg·m⁻³ in austral spring (September and October) as the eddy propagated further to the southwest. Picophytoplankton dominated the community during the spring period, accounting for >50% of the population. As far as is known, this is the first study to investigate temporal changes in chlorophyll a and community structure in a cyclonic eddy propagating across an ocean basin in the southwest Indian Ocean. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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24 pages, 4032 KiB

Open AccessArticle

Accuracy Assessment of Primary Production Models with and without Photoinhibition Using Ocean-Colour Climate Change Initiative Data in the North East Atlantic Ocean

by Polina Lobanova, Gavin H. Tilstone, Igor Bashmachnikov and Vanda Brotas

Remote Sens. 2018, 10(7), 1116; https://doi.org/10.3390/rs10071116 - 12 Jul 2018

Cited by 12 | Viewed by 5988

Abstract

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ ¹⁴C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate [...] Read more.

The accuracy of three satellite models of primary production (PP) of varying complexity was assessed against 95 in situ ¹⁴C uptake measurements from the North East Atlantic Ocean (NEA). The models were run using the European Space Agency (ESA), Ocean Colour Climate Change Initiative (OC-CCI) version 3.0 data. The objectives of the study were to determine which is the most accurate PP model for the region in different provinces and seasons, what is the accuracy of the models using both high (daily) and low (eight day) temporal resolution OC-CCI data, and whether the performance of the models is improved by implementing a photoinhibition function? The Platt-Sathyendranath primary production model (PP_PSM) was the most accurate over all NEA provinces and, specifically, in the Atlantic Arctic province (ARCT) and North Atlantic Drift (NADR) provinces. The implementation of a photoinhibition function in the PP_PSM reduced its accuracy, especially at lower range PP. The Vertical Generalized Production Model-VGPM (PP_VGPM) tended to over-estimate PP, especially in summer and in the NADR. The accuracy of PP_VGPM improved with the implementation of a photoinhibition function in summer. The absorption model of primary production (PP_Aph), with and without photoinhibition, was the least accurate model for the NEA. Mapped images of each model showed that the PP_VGPM was 150% higher in the NADR compared to PP_PSM. In the North Atlantic Subtropical Gyre (NAST) province, PP_Aph was 355% higher than PP_PSM, whereas PP_VGPM was 215% higher. A sensitivity analysis indicated that chlorophyll-a (Chl a), or the absorption of phytoplankton, at 443 nm (a_ph (443)) caused the largest error in the estimation of PP, followed by the photosynthetic rate terms and then the irradiance functions used for each model. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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30 pages, 7406 KiB

Open AccessArticle

Bio-Optical Characterization and Ocean Colour Inversion in the Eastern Lagoon of New Caledonia, South Tropical Pacific

by Luciane Rafaele Favareto, Natália Rudorff, Milton Kampel, Robert Frouin, Rüdiger Röttgers, David Doxaran, Hiroshi Murakami and Cécile Dupouy

Remote Sens. 2018, 10(7), 1043; https://doi.org/10.3390/rs10071043 - 2 Jul 2018

Cited by 13 | Viewed by 5788

Abstract

The Eastern Lagoon of New Caledonia (ELNC) is a semi-enclosed system surrounded by an extensive coral reef barrier. The system has been suffering impacts from climate variability and anthropogenic activities, including mining exploitation. Satellite monitoring is thus an essential tool to detect such [...] Read more.

The Eastern Lagoon of New Caledonia (ELNC) is a semi-enclosed system surrounded by an extensive coral reef barrier. The system has been suffering impacts from climate variability and anthropogenic activities, including mining exploitation. Satellite monitoring is thus an essential tool to detect such changes. The present study aimed to assess the bio-optical variability of the ELNC and examine the applicability of ocean colour algorithms, using in situ bio-optical and radiometric data, collected during the March 2014 CALIOPE 2 cruise. The chlorophyll a concentration (Chla) varied from 0.13–0.72 mg·m⁻³, and the coastal stations were spectrally dominated by non-algal particles (NAP) and coloured dissolved organic matter (CDOM) (>80% of the total non-water absorption at 443 nm), due to the contribution of allochthonous sources. The phytoplankton specific absorption was generally lower (mean, 0.049 m²·mg Chla⁻¹) than typical values observed for the corresponding Chla range, as well as the spectral slopes of the absorption of CDOM plus NAP (a_dg) (mean, 0.016 nm⁻¹) and of the particle backscattering coefficient (b_bp) (mean, 0.07 nm⁻¹). The remote sensing reflectance obtained using two in-water approaches and modelled from Inherent Optical Properties (IOPs) showed less than 20% relative percent differences (RPD). Chla estimates were highly biased for the empirical (OC4 and OC3) and semi-analytical (GSM, QAA, GIOP, LMI) algorithms, especially at the coastal stations. Excluding these stations, the GSM01 yielded the best retrievals with 35–40% RPD. a_dg(443) was well retrieved by all algorithms with ~18% RPD, and b_bp(443) with ~40% RPD. Turbidity algorithms also performed reasonably well (30% RPD), showing the capacity and usefulness of the derived products to monitor the water quality of the ELNC, provided accurate atmospheric correction of the satellite data. Regionally tuned algorithms may potentially improve the Chla retrievals, but better parameterization schemes that consider the spatiotemporal variability of the specific IOPs are still needed. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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25 pages, 11100 KiB

Open AccessArticle

Remote Sensing of Phytoplankton Size Class in Northwest Atlantic from 1998 to 2016: Bio-Optical Algorithms Comparison and Application

by Xiaohan Liu, Emmanuel Devred and Catherine Johnson

Remote Sens. 2018, 10(7), 1028; https://doi.org/10.3390/rs10071028 - 28 Jun 2018

Cited by 10 | Viewed by 5161

Abstract

Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort [...] Read more.

Phytoplankton community structure and phytoplankton size class (PSC) are linked to ecological and biogeochemical changes in the oceanic environment. Many models developed to obtain the fraction of PSCs from satellite remote sensing have only been evaluated in open oceans, and very limited effort has been carried out to report on the performance of these PSC models in productive continental shelf waters. In this study, we evaluated the performance of nine PSC models in the coastal Northwest Atlantic (NWA) by comparison of in situ phytoplankton pigment measurements with coincidental satellite data from the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate-resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging Radiometer Suite (VIIRS). Our results show that no PSC model retrieved all three phytoplankton size classes (pico-, nano-, and micro-phytoplankton) with reliable accuracy in the region of interest. In particular, these PSC models showed poor performance for retrieval of the picophytoplankton fraction of total phytoplankton in our study region, which could be related to the under-representation of pico-dominated samples in the productive waters of the NWA. For the accuracy of retrieved microphytoplankton and combined nano–pico phytoplankton fraction, the regional model developed by Devred et al. (2011) yielded the best result, followed by the model of Brewin et al. (2011). The model of Devred et al. (2011) was applied to satellite-derived chlorophyll-a concentration from the Ocean Color Climate Change Initiative (OC-CCI) archive in the NWA from 1998 to 2016. We report solely on the microphytoplankton biomass and fraction given the inverse relationship that exists with the nano–pico class. The multi-decadal trend along with the deseasonalized trend of microphytoplankton fraction was computed and analyzed for six biogeochemical provinces located in the NWA. Over the 19-year time series, there were significant, positive trends for four of the six provinces, with a slope of 0.36%·yr⁻¹ in the Northwest Continental Shelf (NWCS), 0.25%·yr⁻¹ in the Arctic Waters (ARCT), 0.12%·yr⁻¹ in the Slope Waters (SW) and 0.06%·yr⁻¹ in the Gulf Stream (GFST). Strong positive anomalies of microphytoplankton fraction were found in winter months in NWCS between 2009 and 2014, which could be associated with changes in environmental factors. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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26 pages, 16312 KiB

Open AccessArticle

Using 250-M Surface Reflectance MODIS Aqua/Terra Product to Estimate Turbidity in a Macro-Tidal Harbour: Darwin Harbour, Australia

by Gang Yang, Xiaohua Wang, Elizabeth A. Ritchie, Lulu Qiao, Guangxue Li and Zhixin Cheng

Remote Sens. 2018, 10(7), 997; https://doi.org/10.3390/rs10070997 - 22 Jun 2018

Cited by 12 | Viewed by 6829

Abstract

Turbidity is an indicator of the quality of water and usually exhibits variability associated with changing hydrodynamic conditions, which can be reflected in the sediment dynamics in coastal regions. Darwin Harbour is a typical macro-tidal, well mixed, and complex environment influenced by industries, [...] Read more.

Turbidity is an indicator of the quality of water and usually exhibits variability associated with changing hydrodynamic conditions, which can be reflected in the sediment dynamics in coastal regions. Darwin Harbour is a typical macro-tidal, well mixed, and complex environment influenced by industries, human activities, and natural factors—including winds, currents, river discharges, waves, and tides. As a case study, hydrodynamics and sediment dynamics in Darwin Harbour are investigated using moderate resolution imaging spectroradiometer (MODIS) measurements. This study focuses on understanding the variability of turbidity, mechanisms that control the variations of turbidity and analyzing field data to determine the main factors that influence the sediment dynamics in Darwin Harbour. The results of this study illustrate the seasonal turbidity variation is mainly influenced by the wind waves. The dredging campaigns in 2013 and 2014 wet seasons contributed to the rise of turbidity in Darwin Harbour. The action of tidal currents appears to be the dominant factor controlling the turbidity pattern in a spring–neap cycle and the turbidity intra-tidal variation. In addition, the turbidity maximum zone (TMZ) near Charles Point is formed by the tidal current convergence based on the results of current modelling. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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24 pages, 6646 KiB

Open AccessArticle

Evaluation of MODIS—Aqua Chlorophyll-a Algorithms in the Basilicata Ionian Coastal Waters

by Teodosio Lacava, Emanuele Ciancia, Carmine Di Polito, Alice Madonia, Simone Pascucci, Nicola Pergola, Viviana Piermattei, Valeria Satriano and Valerio Tramutoli

Remote Sens. 2018, 10(7), 987; https://doi.org/10.3390/rs10070987 - 21 Jun 2018

Cited by 12 | Viewed by 5453

Abstract

Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian [...] Read more.

Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian Coastal waters (Ionian Sea, South of Italy). The outputs of one empirical (Med-OC3) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, have been compared with ground measurements acquired during three different measurement campaigns. The achieved results prove the poor accuracy (adjusted R² value of 0.12) of the investigated empirical algorithm and, conversely, the good performance of semi-analytical algorithms (adjusted R² ranging from 0.74 to 0.79). The co-existence of Coloured Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP) has likely determined large errors in the reflectance ratios used in the OCx form algorithms. Finally, a local scale assessment of the bio-optical properties, on the basis of the in situ dataset, allowed for the definition of an operational local scale-tuned version of the MODIS chl-a algorithm, which assured increased accuracy (adjusted R² value of 0.86). Such a tuned algorithm version can provide useful information which can be used by local authorities within regional management systems. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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23 pages, 6257 KiB

Open AccessArticle

Evaluation of the First Year of Operational Sentinel-2A Data for Retrieval of Suspended Solids in Medium- to High-Turbidity Waters

by Isabel Caballero, François Steinmetz and Gabriel Navarro

Remote Sens. 2018, 10(7), 982; https://doi.org/10.3390/rs10070982 - 21 Jun 2018

Cited by 68 | Viewed by 8372

Abstract

In this study, we apply high-resolution Sentinel-2A imagery to assist in the monitoring of the southwestern Spanish coast during its first year of data. The aim is to test suitability of MultiSpectral Imager (MSI) at higher resolution (10 m) for mapping Total Suspended [...] Read more.

In this study, we apply high-resolution Sentinel-2A imagery to assist in the monitoring of the southwestern Spanish coast during its first year of data. The aim is to test suitability of MultiSpectral Imager (MSI) at higher resolution (10 m) for mapping Total Suspended Solids (TSS). Several field campaigns are carried out to collect TSS at three different sites in the Guadalquivir estuary, Cadiz Bay and Conil port. A regional multi-conditional remote sensing algorithm with a switching method that automatically selects the most sensitive TSS vs. water reflectance relationship is developed to estimate TSS concentration while avoiding saturation effects. An existing semi-analytical algorithm is calibrated by means of a cross-validation procedure based on both red 664 nm (r = 0.8, NRMSE of 25.06%) and near-infrared (NIR) 865 nm (r = 0.98, NRMSE of 10.28%) parts of the spectrum, showing the MSI sensor’s great potential to estimate TSS even though it was not designed for aquatic remote sensing. The first year of data reveals improved monitoring along the coastal region at unprecedented resolution with accuracy to detect the Estuarine Turbidity Maximum (ETM). ACOLITE and POLYMER Atmospheric Correction strategies are applied over this coastal region (no in-situ data on water reflectance). The results confirm that the flexible POLYMER algorithm can address intense sun-glint effects. These findings encourage further research of water quality studies relying on both operational Sentinel-2A and Sentinel-2B, with great implications to improve the understanding of turbid coastal and inland water environments. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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18 pages, 9846 KiB

Open AccessArticle

Parameterization of Spectral Particulate and Phytoplankton Absorption Coefficients in Sognefjord and Trondheimsfjord, Two Contrasting Norwegian Fjord Ecosystems

by Veloisa J. Mascarenhas and Oliver Zielinski

Remote Sens. 2018, 10(6), 977; https://doi.org/10.3390/rs10060977 - 20 Jun 2018

Cited by 4 | Viewed by 4897

Abstract

We present here parameterizations of particulate and phytoplankton absorption coefficients as functions of pigment concentrations (Tchla) in Sognefjord and Trondheimsfjord along the northwestern coast of Norway. The total particulate and non-algal optical densities were measured via quantitative filter technique (QFT) in a spectrophotometer [...] Read more.

We present here parameterizations of particulate and phytoplankton absorption coefficients as functions of pigment concentrations (Tchla) in Sognefjord and Trondheimsfjord along the northwestern coast of Norway. The total particulate and non-algal optical densities were measured via quantitative filter technique (QFT) in a spectrophotometer with integrating sphere. The spectral parameter coefficients A(λ) and E(λ) of the power law describing variations of particulate and phytoplankton absorption coefficients as a function of Tchla, were not only different from those provided for open ocean case 1 waters, but also exhibited differences in the two fjords under investigation. Considering the influence of glacial meltwater leading to increased inorganic sediment load in Sognefjord we investigate differences in two different parameterizations, developed by excluding and including inner Sognefjord stations. Tchla are modelled to test the parameterizations and validated against data from the same cruise and that from a repeated campaign. Being less influenced by non-algal particles parameterizations performed well in Trondheimsfjord and yielded high coefficients of determination (R²) of modelled vs. measured Tchla. In Sognefjord, the modelled vs. measured Tchla resulted in better R² with parameter coefficients developed excluding the inner-fjord stations influenced by glacial meltwater influx. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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12 pages, 5460 KiB

Open AccessArticle

Spatio-Temporal Variability of the Habitat Suitability Index for Chub Mackerel (Scomber Japonicus) in the East/Japan Sea and the South Sea of South Korea

by Dabin Lee, SeungHyun Son, Wonkook Kim, Joo Myun Park, Huitae Joo and Sang Heon Lee

Remote Sens. 2018, 10(6), 938; https://doi.org/10.3390/rs10060938 - 13 Jun 2018

Cited by 45 | Viewed by 6341

Abstract

The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift [...] Read more.

The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift in the fishing grounds of several fish species. The habitat suitability index (HSI), a reliable indicator of the capacity of a habitant to support selected species, has been widely used to detect and forecast fishing ground formation. In this study, the catch data of the chub mackerel and satellite-derived environmental factors were used to calculate the HSI for the chub mackerel in the South Sea, South Korea. More than 80% of the total catch was found in areas with an SST of 14.72–25.72 °C, chlorophyll-a of 0.30–0.92 mg m⁻³, and primary production of 523.7–806.46 mg C m⁻² d⁻¹. Based on these results, the estimated climatological monthly HSI from 2002 to 2016 clearly showed that the wintering ground of the chub mackerel generally formed in the South Sea of South Korea, coinciding with the catch distribution during the same period. This outcome implies that our estimated HSI can yield a reliable prediction of the fishing ground for the chub mackerel in the East/Japan Sea and South Sea of South Korea. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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23 pages, 1581 KiB

Open AccessArticle

Extraction of Photosynthesis Parameters from Time Series Measurements of In Situ Production: Bermuda Atlantic Time-Series Study

by Žarko Kovač, Trevor Platt, Shubha Sathyendranath and Michael W. Lomas

Remote Sens. 2018, 10(6), 915; https://doi.org/10.3390/rs10060915 - 9 Jun 2018

Cited by 8 | Viewed by 5182

Abstract

Computing the vertical structure of primary production in ocean ecosystem models requires information about the vertical distribution of available light, chlorophyll concentration and photosynthesis response parameters. Conversely, given information on vertical structure of chlorophyll and light, we can extract photosynthesis parameters from vertical [...] Read more.

Computing the vertical structure of primary production in ocean ecosystem models requires information about the vertical distribution of available light, chlorophyll concentration and photosynthesis response parameters. Conversely, given information on vertical structure of chlorophyll and light, we can extract photosynthesis parameters from vertical profiles of primary production measured at sea, as we illustrate here for the Bermuda Atlantic Time-Series Study. The procedure is based on a model of the production profile, which itself depends on the underwater light field. To model the light field, attenuation coefficients were estimated from measured optical profiles using a simple model of exponential decay of photosynthetically-available irradiance with depth, which accounted for 97% of the variance in the measured optical data. With the underwater light climate known, an analytical solution for the production profile was employed to recover photosynthesis parameters by minimizing the residual model error. The recovered parameters were used to model normalized production profiles and normalized watercolumn production. The model explained 95% of the variance in the measured normalized production at depth and 97% of the variance in measured normalized watercolumn production. A shifted Gaussian function was used to model biomass profiles and accounted for 93% of the variance in measured biomass at depth. An analytical solution for watercolumn production with the shifted Gaussian biomass was also tested. With the recovered photosynthesis parameters, maximum instantaneous growth rates were estimated by using a literature value for the carbon-to-chlorophyll ratio in this region of the Atlantic. An exact relationship between the maximum instantaneous growth rate and the daily growth rate in the ocean was derived. It was shown that calculating the growth rate by dividing the production by the carbon-to-chlorophyll ratio is equivalent to calculating it from the ratio of the final to the initial biomass, even when production is time dependent. Finally, the seasonal cycle of the recovered assimilation number at the Bermuda Station was constructed and analysed. The presented approach enables the estimation of photosynthesis parameters and growth rates from measured production profiles with only a few model assumptions, and increases the utility of in situ primary production measurements. The retrieved parameters have direct applications in satellite-based estimates of primary production from ocean-colour data, of which we give an example. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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20 pages, 5139 KiB

Open AccessArticle

Scratching Beneath the Surface: A Model to Predict the Vertical Distribution of Prochlorococcus Using Remote Sensing

by Priscila K. Lange, Robert J. W. Brewin, Giorgio Dall’Olmo, Glen A. Tarran, Shubha Sathyendranath, Mikhail Zubkov and Heather A. Bouman

Remote Sens. 2018, 10(6), 847; https://doi.org/10.3390/rs10060847 - 29 May 2018

Cited by 20 | Viewed by 6296

Abstract

The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean [...] Read more.

The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean using an empirical model based on data from 13 Atlantic Meridional Transect cruises. Our model uses satellite-derived sea surface temperature (SST), remote-sensing reflectance at 443 and 488 nm, and the water temperature at a depth of 200 m from Argo data. The model divides the population of Prochlorococcus into two groups: ProI, which dominates under high-light conditions associated with the surface, and ProII, which favors low light found near the deep chlorophyll maximum. ProI and ProII are then summed to provide vertical profiles of the concentration of Prochlorococcus cells. This model predicts that Prochlorococcus cells contribute 32 Mt of carbon biomass (7.4 × 10²⁶ cells) to the Atlantic Ocean, concentrated mainly within the subtropical gyres (35%) and areas near the Equatorial Convergence Zone (30%). When projected globally, 3.4 × 10²⁷ Prochlorococcus cells represent 171 Mt of carbon biomass, with 43% of this global biomass allocated to the upper ocean (0–45 m depth). Annual cell standing stocks were relatively stable between the years 2003 and 2014, and the contribution of the gyres varies seasonally as gyres expand and contract, tracking changes in light and temperature, with lowest cell abundances during the boreal and austral winter (1.4 × 10¹³ cells m⁻²), when surface cell concentrations were highest (9.8 × 10⁴ cells mL⁻¹), whereas the opposite scenario was observed in spring–summer (2 × 10¹³ cells m⁻²). This model provides a three-dimensional view of the abundance of Prochlorococcus cells, revealing that Prochlorococcus contributes significantly to total phytoplankton biomass in the Atlantic Ocean, and can be applied using either in situ measurements at the sea surface (r² = 0.83) or remote-sensing observables (r² = 0.58). Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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22 pages, 3910 KiB

Open AccessArticle

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

by Guanhua Zhou, Zhongqi Ma, Shubha Sathyendranath, Trevor Platt, Cheng Jiang and Kang Sun

Remote Sens. 2018, 10(6), 837; https://doi.org/10.3390/rs10060837 - 27 May 2018

Cited by 23 | Viewed by 6849

Abstract

Optical remote sensing of aquatic vegetation in shallow water is an essential aid to ecosystem protection, but it is difficult because the spectral characteristics of the vegetation are sensitive to external features such as water background effects, atmospheric effects, and the structural properties [...] Read more.

Optical remote sensing of aquatic vegetation in shallow water is an essential aid to ecosystem protection, but it is difficult because the spectral characteristics of the vegetation are sensitive to external features such as water background effects, atmospheric effects, and the structural properties of the canopy. A global sensitivity analysis of an aquatic vegetation radiative transfer model provides invaluable background for algorithm development for use in optical remote sensing. Here, we use the extended Fourier Amplitude Sensitivity Test (EFAST) method for the modelling. Four different cases were identified by subdividing the ranges of water depth and leaf area index (LAI) involved. The results indicate that the reflectance of emergent vegetation is affected mainly by the concentrations of chlorophyll a + b in leaves (Cab), leaf inclination distribution function parameter (LIDFa) and LAI. The parameter LAI is influential in sparse vegetation cases whereas Cab and LIDFa are influential in dense vegetation cases. Canopy reflectance for submerged vegetation is dominated by water parameters. Relatively, LAI and Cab are highly sensitive vegetation parameters. The analysis is extended to vegetation index as well, which takes the Sentinel-2A as the reference sensor. It shows that NDAVI (Normalized Difference Aquatic Vegetation Index) is suitable for retrieving LAI in all cases except deep-sparse for emergent vegetation, whereas NDVI (Normalized Difference Vegetation Index) would be better in the deep-sparse case. NDVI, NDAVI and WAVI (Water Adjusted Vegetation Index), respectively, are suitable for retrieving Cab, Car and LIDFa in dense cases. For submerged vegetation, the sensitivity of LAI to NDAVI is relatively high only in the shallow-sparse case. The adjustment factor L in SAVI and WAVI fails to suppress the sensitivity to water constituent parameters. The sensitivity of LAI and Cab to NDVI in deep cases is relatively higher than that to the other indices, which may provide clues for the construction of inversion algorithms in macrophyte remote sensing in the aquatic environment using spectral signatures in the visible and near infrared regions. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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Full article ">Figure 1
The Schematic of vertical structures for emergent vegetation and submerged vegetation. (a) Emergent vegetation; (b) Submerged vegetation. Full article ">Figure 2
The Sentinel-2A spectral response functions of bands (B1–B9) in 400–1000 nm. Full article ">Figure 3
The cumulative plot of normalized total order sensitivity (NST) for emergent vegetation in four different cases. (a), (b), (c), (d) represent the cases of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. The area enclosed by the curve and the horizontal axis (the first parameter in the list, Hw), and between the curves (except Hw) represents the value of NST. Full article ">Figure 4
In four different cases, the curves of high sensitivity parameters varying with wavelength. The columns (a), (b), (c), (d) represent the case of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. (1)–(9) represent the high sensitivity parameters, (1): N, (2): Concentration of chlorophyll a + b (Cab), (3): Concentration of carotenoid (Car), (4): Concentration of dry matter (Cm), (5): Leaf area index (LAI), (6): Leaf inclination distribution function parameter a (LIDFa), (7): Concentration of chlorophyll a, in water (Cchla), (8): Coefficient to calculate scattering of total suspended matter (Btsm), (9): Concentration of suspended matter (SPM). It should be noted that the curves of absorption coefficient of Cab and Car are also shown in (2) and (3). The vertical axis on the right shows the range of the absorption coefficient. Full article ">Figure 5
The cumulative plot of NST for submerged vegetation in four different cases. (a), (b), (c), (d) represent the cases of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. The area enclosed by the curve and the horizontal axis (the first parameter in the list, Hw), and between the curves (except Hw) represents the value of NST. Full article ">Figure 6
In four different cases, the curves of high sensitivity parameters varying with wavelength. The columns (a), (b), (c), (d) represent the case of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. (1)–(9) represent the high sensitivity parameters of the model, (1): The height of the upper water layer (Hw), (2): Concentration of chlorophyll a, in water (Cchla), (3): Coefficient to calculate scattering of total suspended matter (Btsm), (4): Concentration of suspended matter (SPM), (5): Absorption coefficient of CDOM at 375 nm (aCDOM), (6): Plant height (Hp), (7): Concentration of chlorophyll a+b (Cab), (8): Leaf area index (LAI). Full article ">Figure 7
The NST of high sensitivity parameters to vegetation indices for emergent vegetation. (a), (b), (c), (d) represent the case of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. Full article ">Figure 8
The NST of high sensitivity parameters to vegetation indices for submerged vegetation. (a), (b), (c), (d) represent the cases of shallow-sparse, shallow-dense, deep-sparse and deep-dense, respectively. Full article ">

23 pages, 5888 KiB

Open AccessArticle

Phytoplankton Size Structure in Association with Mesoscale Eddies off Central-Southern Chile: The Satellite Application of a Phytoplankton Size-Class Model

by Andrea Corredor-Acosta, Carmen E. Morales, Robert J. W. Brewin, Pierre-Amaël Auger, Oscar Pizarro, Samuel Hormazabal and Valeria Anabalón

Remote Sens. 2018, 10(6), 834; https://doi.org/10.3390/rs10060834 - 25 May 2018

Cited by 22 | Viewed by 7484

Abstract

Understanding the influence of mesoscale and submesoscale features on the structure of phytoplankton is a key aspect in the assessment of their influence on marine biogeochemical cycling and cross-shore exchanges of plankton in Eastern Boundary Current Systems (EBCS). In this study, the spatio-temporal [...] Read more.

Understanding the influence of mesoscale and submesoscale features on the structure of phytoplankton is a key aspect in the assessment of their influence on marine biogeochemical cycling and cross-shore exchanges of plankton in Eastern Boundary Current Systems (EBCS). In this study, the spatio-temporal evolution of phytoplankton size classes (PSC) in surface waters associated with mesoscale eddies in the EBCS off central-southern Chile was analyzed. Chlorophyll-a (Chl-a) size-fractionated filtration (SFF) data from in situ samplings in coastal and coastal transition waters were used to tune a three-component (micro-, nano-, and pico-phytoplankton) model, which was then applied to total Chl-a satellite data (ESA OC-CCI product) in order to retrieve the Chl-a concentration of each PSC. A sea surface, height-based eddy-tracking algorithm was used to identify and track one cyclonic (sC) and three anticyclonic (ssAC1, ssAC2, sAC) mesoscale eddies between January 2014 and October 2015. Satellite estimates of PSC and in situ SFF Chl-a data were highly correlated (0.64 < r < 0.87), although uncertainty values for the microplankton fraction were moderate to high (50 to 100% depending on the metric used). The largest changes in size structure took place during the early life of eddies (~2 months), and no major differences in PSC between eddy center and periphery were found. The contribution of the microplankton fraction was ~50% (~30%) in sC and ssAC1 (ssAC2 and sAC) eddies when they were located close to the coast, while nanoplankton was dominant (~60–70%) and picoplankton almost constant (<20%) throughout the lifetime of eddies. These results suggest that the three-component model, which has been mostly applied in oceanic waters, is also applicable to highly productive coastal upwelling systems. Additionally, the PSC changes within mesoscale eddies obtained by this satellite approach are in agreement with results on phytoplankton size distribution in mesoscale and submesoscale features in this region, and are most likely triggered by variations in nutrient concentrations and/or ratios during the eddies’ lifetimes. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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25 pages, 2171 KiB

Open AccessArticle

Machine Learning Regression Approaches for Colored Dissolved Organic Matter (CDOM) Retrieval with S2-MSI and S3-OLCI Simulated Data

by Ana Belen Ruescas, Martin Hieronymi, Gonzalo Mateo-Garcia, Sampsa Koponen, Kari Kallio and Gustau Camps-Valls

Remote Sens. 2018, 10(5), 786; https://doi.org/10.3390/rs10050786 - 19 May 2018

Cited by 73 | Viewed by 9302

Abstract

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient,

a_{C D O M}

at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is [...] Read more.

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient,

a_{C D O M}

at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance (

R_{r s}

) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches. Statistics comparison with well-established polynomial regression algorithms shows optimistic results for all models and band combinations, highlighting the good performance of the methods, especially the GPR approach, when all bands are used as input. Application to an atmospheric corrected OLCI image using the reflectance derived form the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are provided to interested users. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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27 pages, 4966 KiB

Open AccessFeature PaperArticle

Remotely Sensing the Biophysical Drivers of Sardinella aurita Variability in Ivorian Waters

by Jean-Baptiste Kassi, Marie-Fanny Racault, Brice A. Mobio, Trevor Platt, Shubha Sathyendranath, Dionysios E. Raitsos and Kouadio Affian

Remote Sens. 2018, 10(5), 785; https://doi.org/10.3390/rs10050785 - 18 May 2018

Cited by 15 | Viewed by 6496

Abstract

The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put [...] Read more.

The coastal regions of the Gulf of Guinea constitute one of the major marine ecosystems, producing essential living marine resources for the populations of Western Africa. In this region, the Ivorian continental shelf is under pressure from various anthropogenic sources, which have put the regional fish stocks, especially Sardinella aurita, the dominant pelagic species in Ivorian industrial fishery landings, under threat from overfishing. Here, we combine in situ observations of Sardinella aurita catch, temperature, and nutrient profiles, with remote-sensing ocean-color observations, and reanalysis data of wind and sea surface temperature, to investigate relationships between Sardinella aurita catch and oceanic primary producers (including biomass and phenology of phytoplankton), and between Sardinella aurita catch and environmental conditions (including upwelling index, and turbulent mixing). We show that variations in Sardinella aurita catch in the following year may be predicted, with a confidence of 78%, based on a bilinear model using only physical variables, and with a confidence of 40% when using only biological variables. However, the physics-based model alone is not sufficient to explain the mechanism driving the year-to-year variations in Sardinella aurita catch. Based on the analysis of the relationships between biological variables, we demonstrate that in the Ivorian continental shelf, during the study period 1998–2014, population dynamics of Sardinella aurita, and oceanic primary producers, may be controlled, mainly by top-down trophic interactions. Finally, based on the predictive models constructed here, we discuss how they can provide powerful tools to support evaluation and monitoring of fishing activity, which may help towards the development of a Fisheries Information and Management System. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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21 pages, 8863 KiB

Open AccessArticle

Machine Learning Automatic Model Selection Algorithm for Oceanic Chlorophyll-a Content Retrieval

by Katalin Blix and Torbjørn Eltoft

Remote Sens. 2018, 10(5), 775; https://doi.org/10.3390/rs10050775 - 17 May 2018

Cited by 40 | Viewed by 6519

Abstract

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland [...] Read more.

Ocean Color remote sensing has a great importance in monitoring of aquatic environments. The number of optical imaging sensors onboard satellites has been increasing in the past decades, allowing to retrieve information about various water quality parameters of the world’s oceans and inland waters. This is done by using various regression algorithms to retrieve water quality parameters from remotely sensed multi-spectral data for the given sensor and environment. There is a great number of such algorithms for estimating water quality parameters with different performances. Hence, choosing the most suitable model for a given purpose can be challenging. This is especially the fact for optically complex aquatic environments. In this paper, we present a concept to an Automatic Model Selection Algorithm (AMSA) aiming at determining the best model for a given matchup dataset. AMSA automatically chooses between regression models to estimate the parameter in interest. AMSA also determines the number and combination of features to use in order to obtain the best model. We show how AMSA can be built for a certain application. The example AMSA we present here is designed to estimate oceanic Chlorophyll-a for global and optically complex waters by using four Machine Learning (ML) feature ranking methods and three ML regression models. We use a synthetic and two real matchup datasets to find the best models. Finally, we use two images from optically complex waters to illustrate the predictive power of the best models. Our results indicate that AMSA has a great potential to be used for operational purposes. It can be a useful objective tool for finding the most suitable model for a given sensor, water quality parameter and environment. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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21 pages, 3380 KiB

Open AccessArticle

A Statistical Modeling Framework for Characterising Uncertainty in Large Datasets: Application to Ocean Colour

by Peter E. Land, Trevor C. Bailey, Malcolm Taberner, Silvia Pardo, Shubha Sathyendranath, Kayvan Nejabati Zenouz, Vicki Brammall, Jamie D. Shutler and Graham D. Quartly

Remote Sens. 2018, 10(5), 695; https://doi.org/10.3390/rs10050695 - 2 May 2018

Cited by 4 | Viewed by 6181

Abstract

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is [...] Read more.

Uncertainty estimation is crucial to establishing confidence in any data analysis, and this is especially true for Essential Climate Variables, including ocean colour. Methods for deriving uncertainty vary greatly across data types, so a generic statistics-based approach applicable to multiple data types is an advantage to simplify the use and understanding of uncertainty data. Progress towards rigorous uncertainty analysis of ocean colour has been slow, in part because of the complexity of ocean colour processing. Here, we present a general approach to uncertainty characterisation, using a database of satellite-in situ matchups to generate a statistical model of satellite uncertainty as a function of its contributing variables. With an example NASA MODIS-Aqua chlorophyll-a matchups database mostly covering the north Atlantic, we demonstrate a model that explains 67% of the squared error in log(chlorophyll-a) as a potentially correctable bias, with the remaining uncertainty being characterised as standard deviation and standard error at each pixel. The method is quite general, depending only on the existence of a suitable database of matchups or reference values, and can be applied to other sensors and data types such as other satellite observed Essential Climate Variables, empirical algorithms derived from in situ data, or even model data. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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20 pages, 29419 KiB

Open AccessArticle

Long-Term Changes in Colored Dissolved Organic Matter from Satellite Observations in the Bohai Sea and North Yellow Sea

by Cong Xiao, Deyong Sun, Shengqiang Wang, Zhongfeng Qiu, Yu Huan and Jiabao Zhang

Remote Sens. 2018, 10(5), 688; https://doi.org/10.3390/rs10050688 - 29 Apr 2018

Cited by 4 | Viewed by 4811

Abstract

Spatial and temporal variations in colored dissolved organic matter (CDOM) are of great importance to understanding the dynamics of the biogeochemical properties of water bodies. This study proposed a remote sensing approach for estimating CDOM concentrations (C_CDOM) based on in [...] Read more.

Spatial and temporal variations in colored dissolved organic matter (CDOM) are of great importance to understanding the dynamics of the biogeochemical properties of water bodies. This study proposed a remote sensing approach for estimating CDOM concentrations (C_CDOM) based on in situ observations from the Bohai Sea (BS) and the North Yellow Sea (NYS). Cross-validation demonstrated that the accuracy of the CDOM algorithm is R² = 0.78, APD = 15.9%, RMSE = 0.92 (ppb). The CDOM algorithm was applied to estimate the 14-year (2003–2016) sea surface C_CDOM in the BS and NYS areas using Moderate Resolution Imaging Spectroradiometer (MODIS) monthly products. The results showed a significant fluctuation in CDOM variations on a long-term scale. The highest values of CDOM were observed in the BS, the middle values were observed in the Bohai Strait, and the lowest values were observed in the NYS. Seasonal variations were observed with long-lasting high CDOM values from June to August in coastal waters, while relatively low values were observed in the NYS in the summer. In the spring and fall, a distinct increase appeared in the NYS. High CDOM values in the nearshore coastal waters were mostly related to terrestrial inputs, while CDOM in the offshore regions was mainly due to autochthonous production. Furthermore, ocean currents played an important role in the variations in CDOM in the BS and NYS areas, especially for variations in CDOM in the Bohai Strait. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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12 pages, 3405 KiB

Open AccessArticle

Influence of Tropical Instability Waves on Phytoplankton Biomass near the Marquesas Islands

by Elodie Martinez, Hirohiti Raapoto, Christophe Maes and Keitapu Maamaatuaihutapu

Remote Sens. 2018, 10(4), 640; https://doi.org/10.3390/rs10040640 - 20 Apr 2018

Cited by 8 | Viewed by 5017

Abstract

The Marquesas form an isolated group of small islands in the Central South Pacific where quasi-permanent biological activity is observed. During La Niña events, this biological activity, shown by a net increase of chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), is particularly [...] Read more.

The Marquesas form an isolated group of small islands in the Central South Pacific where quasi-permanent biological activity is observed. During La Niña events, this biological activity, shown by a net increase of chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), is particularly strong. It has been hypothesized that this strong activity is due to iron-rich waters advected from the equatorial region to the Marquesas by tropical instability waves (TIWs). Here we investigate this hypothesis over 18 years by combining satellite observations, re-analyses of ocean data, and Lagrangian diagnostics. Four La Niña events ranging from moderate to strong intensity occurred during this period, and our results show that the Chl plume within the archipelago can be indeed influenced by such equatorial advection, but this was observed during the strong 1998 and 2010 La Niña conditions only. Chl spatio-temporal patterns during the occurrence of other TIWs rather suggest the interaction of large-scale forcing events such as an uplift of the thermocline or the enhancement of coastal upwelling induced by the tropical strengthening of the trades with the islands leading to enhancement of phytoplankton biomass within the surface waters. Overall, whatever the conditions, our analyses suggest that the influence of the TIWs is to disperse, stir, and, therefore, modulate the shape of the existing phytoplankton plume. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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17 pages, 3717 KiB

Open AccessArticle

Comparison of Satellite-Derived Phytoplankton Size Classes Using In-Situ Measurements in the South China Sea

by Shuibo Hu, Wen Zhou, Guifen Wang, Wenxi Cao, Zhantang Xu, Huizeng Liu, Guofeng Wu and Wenjing Zhao

Remote Sens. 2018, 10(4), 526; https://doi.org/10.3390/rs10040526 - 27 Mar 2018

Cited by 17 | Viewed by 5308

Abstract

Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with [...] Read more.

Ocean colour remote sensing is used as a tool to detect phytoplankton size classes (PSCs). In this study, the Medium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) phytoplankton size classes (PSCs) products were compared with in-situ High Performance Liquid Chromatography (HPLC) data for the South China Sea (SCS), collected from August 2006 to September 2011. Four algorithms were evaluated to determine their ability to detect three phytoplankton size classes. Chlorophyll-a (Chl-a) and absorption spectra of phytoplankton (a_ph(λ)) were also measured to help understand PSC’s algorithm performance. Results show that the three abundance-based approaches performed better than the inherent optical property (IOP)-based approach in the SCS. The size detection of microplankton and picoplankton was generally better than that of nanoplankton. A three-component model was recommended to produce maps of surface PSCs in the SCS. For the IOP-based approach, satellite retrievals of inherent optical properties and the PSCs algorithm both have impacts on inversion accuracy. However, for abundance-based approaches, the selection of the PSCs algorithm seems to be more critical, owing to low uncertainty in satellite Chl-a input data Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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25 pages, 7834 KiB

Open AccessArticle

Improving the Remote Sensing Retrieval of Phytoplankton Functional Types (PFT) Using Empirical Orthogonal Functions: A Case Study in a Coastal Upwelling Region

by Marco Correa-Ramirez, Carmen E. Morales, Ricardo Letelier, Valeria Anabalón and Samuel Hormazabal

Remote Sens. 2018, 10(4), 498; https://doi.org/10.3390/rs10040498 - 22 Mar 2018

Cited by 7 | Viewed by 6364

Abstract

An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT) in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a) in [...] Read more.

An approach that improves the spectral-based PHYSAT method for identifying phytoplankton functional types (PFT) in satellite ocean-color imagery is developed and applied to one study case. This new approach, called PHYSTWO, relies on the assumption that the dominant effect of chlorophyll-a (Chl-a) in the normalized water-leaving radiance (nLw) spectrum can be effectively isolated from the signal of accessory pigment biomarkers of different PFT by using Empirical Orthogonal Function (EOF) decomposition. PHYSTWO operates in the dimensionless plane composed by the first two EOF modes generated through the decomposition of a space–nLw matrix at seven wavelengths (412, 443, 469, 488, 531, 547, and 555 nm). PFT determination is performed using orthogonal models derived from the acceptable ranges of anomalies proposed by PHYSAT but adjusted with the available regional and global data. In applying PHYSTWO to study phytoplankton community structures in the coastal upwelling system off central Chile, we find that this method increases the accuracy of PFT identification, extends the application of this tool to waters with high Chl-a concentration, and significantly decreases (~60%) the undetermined retrievals when compared with PHYSAT. The improved accuracy of PHYSTWO and its applicability for the identification of new PFT are discussed. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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18 pages, 4181 KiB

Open AccessArticle

Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes

by Shuibo Hu, Huizeng Liu, Wenjing Zhao, Tiezhu Shi, Zhongwen Hu, Qingquan Li and Guofeng Wu

Remote Sens. 2018, 10(3), 191; https://doi.org/10.3390/rs10030191 - 8 Mar 2018

Cited by 48 | Viewed by 6546

Abstract

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton [...] Read more.

The size of phytoplankton not only influences its physiology, metabolic rates and marine food web, but also serves as an indicator of phytoplankton functional roles in ecological and biogeochemical processes. Therefore, some algorithms have been developed to infer the synoptic distribution of phytoplankton cell size, denoted as phytoplankton size classes (PSCs), in surface ocean waters, by the means of remotely sensed variables. This study, using the NASA bio-Optical Marine Algorithm Data set (NOMAD) high performance liquid chromatography (HPLC) database, and satellite match-ups, aimed to compare the effectiveness of modeling techniques, including partial least square (PLS), artificial neural networks (ANN), support vector machine (SVM) and random forests (RF), and feature selection techniques, including genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination based on support vector machine (SVM-RFE), for inferring PSCs from remote sensing data. Results showed that: (1) SVM-RFE worked better in selecting sensitive features; (2) RF performed better than PLS, ANN and SVM in calibrating PSCs retrieval models; (3) machine learning techniques produced better performance than the chlorophyll-a based three-component method; (4) sea surface temperature, wind stress, and spectral curvature derived from the remote sensing reflectance at 490, 510, and 555 nm were among the most sensitive features to PSCs; and (5) the combination of SVM-RFE feature selection techniques and random forests regression was recommended for inferring PSCs. This study demonstrated the effectiveness of machine learning techniques in selecting sensitive features and calibrating models for PSCs estimations with remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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Full article ">Figure 1
Framework for model development. Full article ">Figure 2
Scatter plots of satellite-derived versus high performance liquid chromatography (HPLC) microplankton size fractions (Fm): (a) random forests using features selected with SVM-RFE, (b) SVM using features selected with SVM-RFE, (c) SVM using ocean color features selected with SVM-RFE, and (d) three-component method. The dashed line is a 1:1 line, and the solid line is a regression line. Plot (e) shows the frequency distributions of their relative errors, and the numbers along the color ramp indicates the pixel density after log transformation (y = ln(x)). Full article ">Figure 3
Scatter plots of satellite-derived versus high performance liquid chromatography (HPLC) nanoplankton size fractions (Fn): (a) random forests using features selected with SVM-RFE, (b) SVM using features selected with SVM-RFE, (c) SVM using ocean color features selected with SVM-RFE, and (d) three-component method. The dashed line is a 1:1 line, and the solid is a regression line. Plot (e) shows the frequency distributions of their relative errors, and the numbers along the color ramp indicates the pixel density after log transformation (y = ln(x)). Full article ">Figure 4
Scatter plots of satellite-derived versus high performance liquid chromatography (HPLC) picoplankton size fractions (Fp): (a) random forests using features selected with SVM-RFE, (b) SVM using features selected with SVM-RFE, (c) SVM using ocean color features selected with SVM-RFE, and (d) three-component method. The dash line is 1:1 line, and the solid is regression line. Plot (e) shows the frequency distributions of their relative errors, and the numbers along the color ramp indicates the pixel density after log transformation (y = ln(x)). Full article ">

17 pages, 3947 KiB

Open AccessArticle

Deriving Total Suspended Matter Concentration from the Near-Infrared-Based Inherent Optical Properties over Turbid Waters: A Case Study in Lake Taihu

by Wei Shi, Yunlin Zhang and Menghua Wang

Remote Sens. 2018, 10(2), 333; https://doi.org/10.3390/rs10020333 - 23 Feb 2018

Cited by 41 | Viewed by 6269

Abstract

Normalized water-leaving radiance spectra nL_w(λ), particle backscattering coefficients b_bp(λ) in the near-infrared (NIR) wavelengths, and total suspended matter (TSM) concentrations over turbid waters are analytically correlated. To demonstrate the use of b_bp(λ [...] Read more.

Normalized water-leaving radiance spectra nL_w(λ), particle backscattering coefficients b_bp(λ) in the near-infrared (NIR) wavelengths, and total suspended matter (TSM) concentrations over turbid waters are analytically correlated. To demonstrate the use of b_bp(λ) in the NIR wavelengths in coastal and inland waters, we used in situ optics and TSM data to develop two TSM algorithms from measurements of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) using backscattering coefficients at the two NIR bands b_bp(745) and b_bp(862) for Lake Taihu. The correlation coefficients between the modeled TSM concentrations from b_bp(745) and b_bp(862) and the in situ TSM are 0.93 and 0.92, respectively. A different in situ dataset acquired between 2012 and 2016 for Lake Taihu was used to validate the performance of the NIR TSM algorithms for VIIRS-SNPP observations. TSM concentrations derived from VIIRS-SNPP observations with these two NIR b_bp(λ)-based TSM algorithms matched well with in situ TSM concentrations in Lake Taihu between 2012 and 2016. The normalized root mean square errors (NRMSEs) for the two NIR algorithms are 0.234 and 0.226, respectively. The two NIR-based TSM algorithms are used to compute the satellite-derived TSM concentrations to study the seasonal and interannual variability of the TSM concentration in Lake Taihu between 2012 and 2016. In fact, the NIR-based TSM algorithms are analytically based with minimal in situ data to tune the coefficients. They are not sensitive to the possible nL_w(λ) saturation in the visible bands for highly turbid waters, and have the potential to be used for estimation of TSM concentrations in turbid waters with similar NIR nL_w(λ) spectra as those in Lake Taihu. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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15 pages, 15256 KiB

Open AccessArticle

Chlorophyll-a Concentration Retrieval in the Optically Complex Waters of the St. Lawrence Estuary and Gulf Using Principal Component Analysis

by Julien Laliberté, Pierre Larouche, Emmanuel Devred and Susanne Craig

Remote Sens. 2018, 10(2), 265; https://doi.org/10.3390/rs10020265 - 8 Feb 2018

Cited by 21 | Viewed by 8101

Abstract

Empirical methods based on band ratios to infer chlorophyll-a concentration by satellite do not perform well over the optically complex waters of the St. Lawrence Estuary and Gulf. Using a dataset of 93 match-ups, we explore an alternative method relying on empirical orthogonal [...] Read more.

Empirical methods based on band ratios to infer chlorophyll-a concentration by satellite do not perform well over the optically complex waters of the St. Lawrence Estuary and Gulf. Using a dataset of 93 match-ups, we explore an alternative method relying on empirical orthogonal functions (EOF) to develop an algorithm that relates the satellite-derived remote sensing reflectances to in situ chlorophyll-a concentration for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Results show that an accuracy of 41% at retrieving chlorophyll-a concentration can be reached using the EOF method compared to 140% for the widely-used Ocean Chlorophyll 4 (OC4v4) empirical algorithm, 53% for the Garver-Siegel-Maritorena (GSM01) and 54% for the Generalized Inherent Optical Property (GIOP) semi-analytical algorithms. This result is possible because the EOF approach is able to extract region-specific radiometric features from the satellite remote sensing reflectances that are related to absorption properties of optical components (water, coloured dissolved organic matter and chlorophyll-a) using the visible SeaWiFS channels. The method could easily be used with other ocean-colour satellite sensors (e.g., MODIS, MERIS, VIIRS, OLCI) to extend the time series for the St. Lawrence Estuary and Gulf waters. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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Full article ">Figure 1
Frequency distribution of the 2927 original Chl measurements, 93 of which were considered valid match-ups. (a) Chl concentration frequency distributions (inset presents the same data binned by order of magnitude). Temporal distributions of Chl concentration for (b) the original and (c) match-up datasets. Full article ">Figure 2
Representation of the different water-types. (a) Classification of the SLEG climatological Rrs using the methodology of [<a href="#B37-remotesensing-10-00265" class="html-bibr">37</a>]. The black line represents the perfect Case-1 relation between Rrs(412)/Rrs(443) and Rrs(555)/Rrs(490). (b) Map of the normalized difference between the climatological SeaWiFS Rrs spectra and the perfect Case-1 line (see the text), with black crosses representing the location of the original Chl samples and grey dots representing the retained Chl samples. The same colour scale applies to both panels. Full article ">Figure 3
Band ratio algorithms. (a) Comparison of the St. Lawrence Estuary and Gulf (SLEG) match-ups (black dots) and SeaBASS [<a href="#B55-remotesensing-10-00265" class="html-bibr">55</a>] datasets (light grey dots). The grey line corresponds to the OC4v4 polynomial fit, and the black line is the linear regression of in situ Chl and corresponding SLEG remote sensing reflectances ratios, with R443/555 = Rrs(443)/Rrs(555), R490/555 = Rrs(490)/Rrs(555) and R510/555 = Rrs(510)/Rrs(555). (b) Scatterplot of in situ versus satellite-derived Chl using the OC4L algorithm. Full article ">Figure 4
Features of the EOF method: (a) stability of the EOF; (b) scatter plot of in situ Chl versus satellite-derived Chl using the EOF, with the dashed line as the 1:1 ratio; (c) all modes of oscillation (solid coloured lines are linear interpolation over wavelengths of the discrete spectral data and are used as a guide to aid visualizing the spectral signature). Full article ">Figure 5
Relative error as a function of (a) Chl concentration and (b) spatial distribution. Full article ">Figure 6
SLEG chlorophyll for 12 October 2003, as predicted by the EOF algorithm. Full article ">

18 pages, 2563 KiB

Open AccessArticle

Hue-Angle Product for Low to Medium Spatial Resolution Optical Satellite Sensors

by Hendrik Jan Van der Woerd and Marcel Robert Wernand

Remote Sens. 2018, 10(2), 180; https://doi.org/10.3390/rs10020180 - 26 Jan 2018

Cited by 81 | Viewed by 8868

Abstract

In the European Citclops project, with a prime aim of developing new tools to involve citizens in the water quality monitoring of natural waters, colour was identified as a simple property that can be measured via a smartphone app and by dedicated low-cost [...] Read more.

In the European Citclops project, with a prime aim of developing new tools to involve citizens in the water quality monitoring of natural waters, colour was identified as a simple property that can be measured via a smartphone app and by dedicated low-cost instruments. In a recent paper, we demonstrated that colour, as expressed mainly by the hue angle (α), can also be derived accurately and consistently from the ocean colour satellite instruments that have observed the Earth since 1997. These instruments provide superior temporal coverage of natural waters, albeit at a reduced spatial resolution of 300 m at best. In this paper, the list of algorithms is extended to the very first ocean colour instrument, and the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution product. In addition, we explore the potential of the hue angle derivation from multispectral imaging instruments with a higher spatial resolution but reduced spectral resolution: the European Space Agency (ESA) multispectral imager (MSI) on Sentinel-2 A,B, the Operational Land Imager (OLI) on the National Aeronautics and Space Administration (NASA) Landsat-8, and its precursor, the Enhanced Thematic Mapper Plus (ETM+) on Landsat-7. These medium-resolution imagers might play a role in an upscaling from point measurements to the typical 1-km pixel size from ocean colour instruments. As the parameter α (the colour hue angle) is fairly new to the community of water remote sensing scientists, we present examples of how colour can help in the image analysis in terms of water-quality products. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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22 pages, 3246 KiB

Open AccessArticle

High-Chlorophyll-Area Assessment Based on Remote Sensing Observations: The Case Study of Cape Trafalgar

by Iria Sala, Gabriel Navarro, Marina Bolado-Penagos, Fidel Echevarría and Carlos M. García

Remote Sens. 2018, 10(2), 165; https://doi.org/10.3390/rs10020165 - 25 Jan 2018

Cited by 19 | Viewed by 5101

Abstract

Cape Trafalgar has been highlighted as a hotspot of high chlorophyll concentrations, as well as a source of biomass for the Alborán Sea. It is located in an unique geographical framework between the Gulf of Cádiz (GoC), which is dominated by long-term seasonal [...] Read more.

Cape Trafalgar has been highlighted as a hotspot of high chlorophyll concentrations, as well as a source of biomass for the Alborán Sea. It is located in an unique geographical framework between the Gulf of Cádiz (GoC), which is dominated by long-term seasonal variability, and the Strait of Gibraltar, which is mainly governed by short-term tidal variability. Furthermore, here bathymetry plays an important role in the upwelling of nutrient-rich waters. In order to study the spatial and temporal variability of chlorophyll-a in this region, 10 years of ocean colour observations using the MEdium Resolution Imaging Spectrometer (MERIS) were analysed through different approaches. An empirical orthogonal function decomposition distinguished two coastal zones with opposing phases that were analysed by wavelet methods in order to identify their temporal variability. In addition, to better understand the physical–biological interaction in these zones, the co-variation between chlorophyll-a and different environmental variables (wind, river discharge, and tidal current) was analysed. Zone 1, located on the GoC continental shelf, was characterised by a seasonal variability weakened by the influence of other environmental variables. Meanwhile, Zone 2, which represented the dynamics in Cape Trafalgar but did not show any clear pattern of variability, was strongly correlated with tidal current whose variability was probably determined by other drivers. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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13 pages, 23574 KiB

Open AccessTechnical Note

Annual New Production of Phytoplankton Estimated from MODIS-Derived Nitrate Concentration in the East/Japan Sea

by Huitae Joo, Dabin Lee, Seung Hyun Son and Sang Heon Lee

Remote Sens. 2018, 10(5), 806; https://doi.org/10.3390/rs10050806 - 22 May 2018

Cited by 7 | Viewed by 5441

Abstract

Our main objective in this study was to determine the inter-annual variation of the annual new production in the East/Japan Sea (EJS), which was estimated from MODIS-aqua satellite-derived sea surface nitrate (SSN). The new production was extracted from northern (>40° N) and southern [...] Read more.

Our main objective in this study was to determine the inter-annual variation of the annual new production in the East/Japan Sea (EJS), which was estimated from MODIS-aqua satellite-derived sea surface nitrate (SSN). The new production was extracted from northern (>40° N) and southern (>40° N) part of EJS based on Sub Polar Front (SPF). Based on the SSN concentrations derived from satellite data, we found that the annual new production (Mean ± S.D = 85.6 ± 10.1 g C m⁻² year⁻¹) in the northern part of the EJS was significantly higher (t-test, p < 0.01) than that of the southern part of the EJS (Mean ± S.D = 65.6 ± 3.9 g C m⁻² year⁻¹). Given the relationships between the new productions and sea surface temperature (SST) in this study, the new production could be more susceptible in the northern part than the southern part of the EJS under consistent SST warming. Since the new production estimated in this study is only based on the nitrate inputs into the euphotic depths during the winter, new productions from additional nitrate sources (e.g., the nitrate upward flux through the MLD and atmospheric deposition) should be considered for estimating the annual new production. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour)

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