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Remote Sensing of Ocean Colour: Theory and Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 69123

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Special Issue Editors

Prof. Dr. Trevor Platt

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Guest Editor

Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
Interests: the physiological ecology of marine phytoplankton; structure and function of the marine ecosystem; submarine optics; remote sensing of ocean colour; the ocean carbon cycle and climate change, and the ecological approach to fisheries management
Special Issues, Collections and Topics in MDPI journals

Dr. Shubha Sathyendranath

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Guest Editor

Plymouth Marine Laboratory, Plymouth PL1 3DH, UK
Interests: ocean colour modelling; spectral characteristics of light penetration underwater; bio-optical properties of phytoplankton; modelling primary production; bio-geochemical cycles in the sea; climate change; biological–physical interactions in the marine system; ecological provinces in the sea; ecological indicators and phytoplankton functional types
Special Issues, Collections and Topics in MDPI journals

Dr. Heather Bouman

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Guest Editor

Department of Earth Sciences, University of Oxford, Oxford OX1 3AN, UK
Interests: polar ecosystems; community structure of phytoplankton; meta-analysis of phytoplankton data; bio-optical properties of phytoplankton; and primary production

Dr. David McKee

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Guest Editor

Department of Physics, University of Strathclyde, Glasgow, G1 1XQ, UK
Interests: optical sensors; remote sensing of ocean colour; radiative transfer in the ocean; propagation of measurement uncertainties; and optically complex waters

Dr. Carsten Brockmann

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Guest Editor

Brockmann Consult GmbH, 21502 Geesthacht, Germany
Interests: optical remote sensing; radiative transfer; remote sensing of water quality; remote sensing and climate change; calibration and validation of remotely-sensed signals; software design for earth observation (EO) data processing and image analysis; and analysis ready data

Special Issue Information

The Special Issue will deal with the theory and applications of ocean-colour remote sensing. The scope will include, but not be restricted to the following:

Optical theory for ocean colour
Atmospheric correction
Calibration and validation of ocean-colour signals
The underwater light field
Ocean colour in optically-complex waters
Ocean colour in lakes
Sensors, instruments, and micro-satellite missions for ocean colour
Ocean colour and ocean biogeochemistry
Ocean colour and ecological indicators; ocean colour for reporting to legislation
Ocean colour and the spatial structure of the global marine ecosystem
Analysis of time series of ocean-colour products
Estimation of primary production from ocean colour

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Earth observation
Remote sensing
Ocean colour
Phytoplankton
Marine optics

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Published Papers (11 papers)

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Editorial

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3 pages, 170 KiB

Open AccessEditorial

Special Issue on Remote Sensing of Ocean Color: Theory and Applications

by Trevor Platt, Shubha Sathyendranath, Heather Bouman, Carsten Brockmann and David McKee

Sensors 2020, 20(12), 3445; https://doi.org/10.3390/s20123445 - 18 Jun 2020

Cited by 3 | Viewed by 2709

Abstract

The editorial team are delighted to present this Special Issue of Sensors focused on Remote Sensing of Ocean Color: Theory and Applications. We believe that this is a timely opportunity to showcase current developments across a broad range of topics in ocean color remote sensing (OCRS). Although the field is well-established, in this Special Issue we are able to highlight advances in the applications of the technology, our understanding of the underpinning science, and its relevance in the context of monitoring climate change and engaging public participation. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

Research

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11 pages, 3461 KiB

Open AccessEditor’s ChoiceArticle

Modelling Remote Sensing Reflectance to Detect Dispersed Oil at Sea

by Emilia Baszanowska, Zbigniew Otremba and Jacek Piskozub

Sensors 2020, 20(3), 863; https://doi.org/10.3390/s20030863 - 6 Feb 2020

Cited by 18 | Viewed by 3113

Abstract

This paper presents a model of upwelling radiation above the seawater surface in the event of a threat of dispersed oil. The Monte Carlo method was used to simulate a large number of solar photons in the water, eventually obtaining values of remote sensing reflectance (R_rs). Analyses were performed for the optical properties of seawater characteristic for the Gulf of Gdańsk (southern Baltic Sea). The case of seawater contaminated by dispersed oil at a concentration of 10 ppm was also discussed for different wind speeds. Two types of oils with extremely different optical properties (refraction and absorption coefficients) were taken into account for consideration. The optical properties (absorption and scattering coefficients and angular light scattering distribution) of the oil-in-water dispersion system were determined using the Mie theory. The spectral index for oil detection in seawater for different wind conditions was determined based on the results obtained for reflectance at selected wavelengths in the range 412–676 nm. The determined spectral index for seawater free of oil achieves higher values for seawater contaminated by oil. The analysis of the values of the spectral indices calculated for 28 combinations of wavelengths was used to identify the most universal spectral index of R_rs for 555 nm/440 nm for dispersed oil detection using any optical parameters. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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22 pages, 7274 KiB

Open AccessArticle

Validation and Comparison of Water Quality Products in Baltic Lakes Using Sentinel-2 MSI and Sentinel-3 OLCI Data

by Tuuli Soomets, Kristi Uudeberg, Dainis Jakovels, Agris Brauns, Matiss Zagars and Tiit Kutser

Sensors 2020, 20(3), 742; https://doi.org/10.3390/s20030742 - 29 Jan 2020

Cited by 108 | Viewed by 8062

Abstract

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R² 0.84–0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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31 pages, 6411 KiB

Open AccessArticle

An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

by Shubha Sathyendranath, Robert J.W. Brewin, Carsten Brockmann, Vanda Brotas, Ben Calton, Andrei Chuprin, Paolo Cipollini, André B. Couto, James Dingle, Roland Doerffer, Craig Donlon, Mark Dowell, Alex Farman, Mike Grant, Steve Groom, Andrew Horseman, Thomas Jackson, Hajo Krasemann, Samantha Lavender, Victor Martinez-Vicente, Constant Mazeran, Frédéric Mélin, Timothy S. Moore, Dagmar Müller, Peter Regner, Shovonlal Roy, Chris J. Steele, François Steinmetz, John Swinton, Malcolm Taberner, Adam Thompson, André Valente, Marco Zühlke, Vittorio E. Brando, Hui Feng, Gene Feldman, Bryan A. Franz, Robert Frouin, Richard W. Gould, Stanford B. Hooker, Mati Kahru, Susanne Kratzer, B. Greg Mitchell, Frank E. Muller-Karger, Heidi M. Sosik, Kenneth J. Voss, Jeremy Werdell and Trevor Platt Show full author list Hide full author list

Sensors 2019, 19(19), 4285; https://doi.org/10.3390/s19194285 - 3 Oct 2019

Cited by 304 | Viewed by 14637

Abstract

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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25 pages, 8446 KiB

Open AccessArticle

The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

by Robert J. W. Brewin, Stefano Ciavatta, Shubha Sathyendranath, Jozef Skákala, Jorn Bruggeman, David Ford and Trevor Platt

Sensors 2019, 19(19), 4182; https://doi.org/10.3390/s19194182 - 26 Sep 2019

Cited by 18 | Viewed by 3749

Abstract

We present a model that estimates the spectral phytoplankton absorption coefficient (

a_{p h} (λ)

) of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature [...] Read more.

We present a model that estimates the spectral phytoplankton absorption coefficient (

a_{p h} (λ)

a_{p h} (λ)

(at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total

a_{p h} (λ)

with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine

a_{p h} (λ)

for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific

a_{p h} (λ)

using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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25 pages, 6069 KiB

Open AccessFeature PaperArticle

Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application

by Sławomir B. Woźniak, Mirosław Darecki and Sławomir Sagan

Sensors 2019, 19(18), 4043; https://doi.org/10.3390/s19184043 - 19 Sep 2019

Cited by 9 | Viewed by 4538

Abstract

Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra R_rs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient b_b(620) directly from the magnitude of R_rs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water’s colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the R_rs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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27 pages, 5490 KiB

Open AccessArticle

Evaluation of Sentinel-3A OLCI Products Derived Using the Case-2 Regional CoastColour Processor over the Baltic Sea

by Dmytro Kyryliuk and Susanne Kratzer

Sensors 2019, 19(16), 3609; https://doi.org/10.3390/s19163609 - 19 Aug 2019

Cited by 73 | Viewed by 7537

Abstract

In this study, the Level-2 products of the Ocean and Land Colour Instrument (OLCI) data on Sentinel-3A are derived using the Case-2 Regional CoastColour (C2RCC) processor for the SentiNel Application Platform (SNAP) whilst adjusting the specific scatter of Total Suspended Matter (TSM) for the Baltic Sea in order to improve TSM retrieval. The remote sensing product “kd_z90max” (i.e., the depth of the water column from which 90% of the water-leaving irradiance are derived) from C2RCC-SNAP showed a good correlation with in situ Secchi depth (SD). Additionally, a regional in-water algorithm was applied to derive SD from the attenuation coefficient K_d(489) using a local algorithm. Furthermore, a regional in-water relationship between particle scatter and bench turbidity was applied to generate turbidity from the remote sensing product “iop_bpart” (i.e., the scattering coefficient of marine particles at 443 nm). The spectral shape of the remote sensing reflectance (R_rs) data extracted from match-up stations was evaluated against reflectance data measured in situ by a tethered Attenuation Coefficient Sensor (TACCS) radiometer. The L2 products were evaluated against in situ data from several dedicated validation campaigns (2016–2018) in the NW Baltic proper. All derived L2 in-water products were statistically compared to in situ data and the results were also compared to results for MERIS validation from the literature and the current S3 Level-2 Water (L2W) standard processor from EUMETSAT. The Chl-a product showed a substantial improvement (MNB 21%, RMSE 88%, APD 96%, n = 27) compared to concentrations derived from the Medium Resolution Imaging Spectrometer (MERIS), with a strong underestimation of higher values. TSM performed within an error comparable to MERIS data with a mean normalized bias (MNB) 25%, root-mean square error (RMSE) 73%, average absolute percentage difference (APD) 63% n = 23). Coloured Dissolved Organic Matter (CDOM) absorption retrieval has also improved substantially when using the product “iop_adg” (i.e., the sum of organic detritus and Gelbstoff absorption at 443 nm) as a proxy (MNB 8%, RMSE 56%, APD 54%, n = 18). The local SD (MNB 6%, RMSE 62%, APD 60%, n = 35) and turbidity (MNB 3%, RMSE 35%, APD 34%, n = 29) algorithms showed very good agreement with in situ data. We recommend the use of the SNAP C2RCC with regionally adjusted TSM-specific scatter for water product retrieval as well as the regional turbidity algorithm for Baltic Sea monitoring. Besides documenting the evaluation of the C2RCC processor, this paper may also act as a handbook on the validation of Ocean Colour data. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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13 pages, 1882 KiB

Open AccessArticle

Detection of Phytoplankton Temporal Anomalies Based on Satellite Inherent Optical Properties: A Tool for Monitoring Phytoplankton Blooms

by Jesús Antonio Aguilar-Maldonado, Eduardo Santamaría-del-Ángel, Adriana Gonzalez-Silvera and María Teresa Sebastiá-Frasquet

Sensors 2019, 19(15), 3339; https://doi.org/10.3390/s19153339 - 30 Jul 2019

Cited by 17 | Viewed by 3991

Abstract

The baseline of a specific variable defines the average behavior of that variable and it must be built from long data series that represent its spatial and temporal variability. In coastal and marine waters, phytoplankton can produce blooms characterized by a wide range of total cells number or chlorophyll a concentration. Classifying a phytoplankton abundance increase as a bloom depends on the species, the study area and the season. The objective of this study was to define the baseline of satellite absorption coefficients in Todos Santos Bay (Baja California, Mexico) to determine the presence of phytoplankton blooms based on the satellite inherent optical properties index (satellite IOP index). Two field points were selected according to historical bloom reports. To build the baseline, the data of phytoplankton absorption coefficients (

a_{p h y,_{G I O P}}

) and detritus plus colored dissolved organic matter (CDOM) (

a_{d C D O M,_{G I O P}}

) from the generalized inherent optical property (GIOP) satellite model of the NASA moderate resolution imaging spectroradiometer (MODIS-Aqua) sensor was studied for the period 2003 to 2016. Field data taken during a phytoplankton bloom event on June 2017 was used to validate the use of satellite products. The association between field and satellite data had a significant positive correlation. The satellite baseline detected a trend change from high values to low values of the satellite IOP index since 2010. Improved wastewater treatment to waters discharged into the Bay, and increased aquaculture of filter-feeding mollusks could have been the cause. The methodology proposed in this study can be a supplementary tool for permanent in situ monitoring programs. This methodology offers several advantages: A complete spatial coverage of the specific coastal area under study, appropriate temporal resolution and a tool for building an objective baseline to detect deviation from average conditions during phytoplankton bloom events. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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26 pages, 5564 KiB

Open AccessArticle

A Printable Device for Measuring Clarity and Colour in Lake and Nearshore Waters

by Robert J. W. Brewin, Thomas G. Brewin, Joseph Phillips, Sophie Rose, Anas Abdulaziz, Werenfrid Wimmer, Shubha Sathyendranath and Trevor Platt

Sensors 2019, 19(4), 936; https://doi.org/10.3390/s19040936 - 22 Feb 2019

Cited by 30 | Viewed by 10926

Abstract

Two expanding areas of science and technology are citizen science and three-dimensional (3D) printing. Citizen science has a proven capability to generate reliable data and contribute to unexpected scientific discovery. It can put science into the hands of the citizens, increasing understanding, promoting environmental stewardship, and leading to the production of large databases for use in environmental monitoring. 3D printing has the potential to create cheap, bespoke scientific instruments that have formerly required dedicated facilities to assemble. It can put instrument manufacturing into the hands of any citizen who has access to a 3D printer. In this paper, we present a simple hand-held device designed to measure the Secchi depth and water colour (Forel Ule scale) of lake, estuarine and nearshore regions. The device is manufactured with marine resistant materials (mostly biodegradable) using a 3D printer and basic workshop tools. It is inexpensive to manufacture, lightweight, easy to use, and accessible to a wide range of users. It builds on a long tradition in optical limnology and oceanography, but is modified for ease of operation in smaller water bodies, and from small watercraft and platforms. We provide detailed instructions on how to build the device and highlight examples of its use for scientific education, citizen science, satellite validation of ocean colour data, and low-cost monitoring of water clarity, colour and temperature. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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16 pages, 13923 KiB

Open AccessArticle

Geometric Correction for the Geostationary Ocean Color Imager from a Combination of Shoreline Matching and Frequency Matching

by Han-Gyeol Kim, Jong-Hwan Son and Taejung Kim

Sensors 2018, 18(11), 3599; https://doi.org/10.3390/s18113599 - 23 Oct 2018

Cited by 8 | Viewed by 3223

Abstract

Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth’s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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15 pages, 2115 KiB

Open AccessTechnical Note

Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations

by Marco Bellacicco, Vincenzo Vellucci, Michele Scardi, Marie Barbieux, Salvatore Marullo and Fabrizio D’Ortenzio

Sensors 2019, 19(13), 3032; https://doi.org/10.3390/s19133032 - 9 Jul 2019

Cited by 17 | Viewed by 4796

Abstract

Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of “regression method” (i.e., either type-I or type-II) to derive bio-optical relationships, both from theoretical perspectives and by using specific examples. We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (b_bp), and 19 years of monthly TChla and b_bp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (C_phyto) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in C_phyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method. Full article

(This article belongs to the Special Issue Remote Sensing of Ocean Colour: Theory and Applications)

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