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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 22135

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Special Issue Editors

Dr. Bradley Penta

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Guest Editor

Naval Research Laboratory, Stennis Space Center, MS, USA
Interests: coupled physical-biological-optical modeling; harmful algal blooms; Gulf of Mexico hypoxia; bio-physical interactions; remote sensing; trophic level interactions

Prof. Dr. Victor S. Kuwahara

E-Mail Website
Guest Editor

Graduate School of Engineering, Soka University, Hachioji, Japan
Interests: biological oceanography; environmental optics, apparent and inherent optical properties in marine ecosystems

Special Issue Information

Dear Colleagues,

Optical remote sensing of the ocean has made significant advancements in recent years with the emergence of new sensors and platforms. However, algorithms that utilize the new remotely sensed data, and provide ocean color-derived products for scientific, regulatory, and commercial uses need to be continuously calibrated and validated with commensurate in situ bio-optical datasets. In situ bio-optical data sets that cover multiple spatial and temporal scales are critical for reliable global extrapolations.

Consequently, in this Special Issue, we encourage the submission of manuscripts focusing on the development and validation of new bio-optical remote sensing algorithms for oceanic and coastal waters. The goal of this Special Issue is to emphasize the essential criticality of coupling remote sensing measurements and algorithms with in situ measurements.

Authors are encouraged to submit articles concerning, but not limited to, the following topics:

In situ validation of ocean color products
Vicarious validation of ocean color sensors
Hyperspectral remote sensing algorithm development
Ocean color products related to:
o Harmful algal bloom (HAB) detection and tracking
o Phytoplankton functional types (PFT)
o Higher trophic levels
o Optical water mass classification
o Biogeochemical cycles

New and future applications of remote sensing ocean color sensors

Dr. Bradley Penta
Prof. Dr. Victor S. Kuwahara
Guest Editors

Manuscript Submission Information

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Keywords

Ocean color
Bio‐optical algorithms
Optically complex waters
Vicarious calibration
In situ validation
Apparent optical properties (AOPs)
Inherent optical properties (IOPs)
Phytoplankton functional types (PFTs)

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Published Papers (6 papers)

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Research

Jump to: Review, Other

27 pages, 7194 KB

Open AccessArticle

Retrieving Pigment Concentrations Based on Hyperspectral Measurements of the Phytoplankton Absorption Coefficient in Global Oceans

by Jing Teng, Tinglu Zhang, Kunpeng Sun and Hong Gao

Remote Sens. 2022, 14(15), 3516; https://doi.org/10.3390/rs14153516 - 22 Jul 2022

Cited by 6 | Viewed by 3041

Abstract

Phytoplankton communities, which can be easily observed by optical sensors deployed on various types of platforms over diverse temporal and spatial scales, are crucial to marine ecosystems and biogeochemical cycles, and accurate pigment concentrations make it possible to effectively derive information from them. [...] Read more.

a_{ph}

) with acceptable accuracy and robustness in global oceans. In this study, a novel method, which is a stepwise regression method improved by early stopping (the ES-SR method) based on the derivative of hyperspectral

a_{ph}

, was proposed to retrieve pigment concentrations. This method was developed from an extensive global dataset collected from layers at different depths and contains phytoplankton pigment concentrations and

a_{ph}

. In the case of the logarithm, strong correlations were found between phytoplankton pigment concentrations and the absolute values of the second derivative (

a_{ph}^{″}

)/the fourth derivative (

a_{ph}^{(4)}

) of

a_{ph}

. According to these correlations, the ES-SR method is effective in obtaining the characteristic wavelengths of phytoplankton pigments for pigment concentration inversion. Compared with the Gaussian decomposition method and principal component regression method, which are based on the derivatives, the ES-SR method implemented on

a_{ph}^{″}

is the optimum approach with the greatest accuracy for each phytoplankton pigment. More than half of the determination coefficient values (

R^{2}_{\log}

) for all pigments, which were retrieved by performing the ES-SR method on

a_{ph}^{″}

, exceeded 0.7. The values retrieved for all pigments fit well to the one-to-one line with acceptable root mean square error (

{RMSE}_{\log}

: 0.146–0.508) and median absolute percentage error (

{MPE}_{\log}

: 8.2–28.5%) values. Furthermore, the poor correlations between the deviations from the values retrieved by the ES-SR method and impact factors related to pigment composition and cell size class show that this method has advantageous robustness. Therefore, the ES-SR method has the potential to effectively monitor phytoplankton community information from hyperspectral optical data in global oceans. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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23 pages, 7792 KB

Open AccessArticle

Underway Hyperspectral Bio-Optical Assessments of Phytoplankton Size Classes in the River-Influenced Northern Gulf of Mexico

by Neeharika Verma, Steven Lohrenz, Sumit Chakraborty and Cédric G. Fichot

Remote Sens. 2021, 13(17), 3346; https://doi.org/10.3390/rs13173346 - 24 Aug 2021

Cited by 5 | Viewed by 3174

Abstract

High inflows of freshwater from the Mississippi and Atchafalaya rivers into the northern Gulf of Mexico during spring contribute to strong physical and biogeochemical gradients which, in turn, influence phytoplankton community composition across the river plume–ocean mixing zone. Spectral features representative of bio-optical signatures of phytoplankton size classes (PSCs) were retrieved from underway, shipboard hyperspectral measurements of above-water remote sensing reflectance using the quasi-analytical algorithm (QAA_v6) and validated against in situ pigment data and spectrophotometric analyses of phytoplankton absorption. The results shed new light on sub-km scale variability in PSCs associated with dynamic and spatially heterogeneous environmental processes in river-influenced oceanic waters. Our findings highlight the existence of localized regions of dominant picophytoplankton communities associated with river plume fronts in both the Mississippi and Atchafalaya rivers in an area of the coastal margin that is otherwise characteristically dominated by larger microphytoplankton. This study demonstrates the applicability of underway hyperspectral observations for providing insights about small-scale physical-biological dynamics in optically complex coastal waters. Fine-scale observations of phytoplankton communities in surface waters as shown here and future satellite retrievals of hyperspectral data will provide a novel means of exploring relationships between physical processes of river plume–ocean mixing and frontal dynamics on phytoplankton community composition. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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17 pages, 7572 KB

Open AccessArticle

Performance Evaluation of Four Ocean Reflectance Model

by Jun Li, Tongji Li, Qingjun Song and Chaofei Ma

Remote Sens. 2021, 13(14), 2748; https://doi.org/10.3390/rs13142748 - 13 Jul 2021

Cited by 1 | Viewed by 2220

Abstract

Phytoplankton are the main factors influencing light under the sea surface in Case Ι water. The ocean reflectance model (ORM), which takes into account the chlorophyll a concentration data, can calculate the remote sensing reflectance of Case Ι water. In this study, we examined the differences and performance of four ORMs, including Morel and Maritorena (2001, MM01), Morel and Gentili (2007, MG07), Mobley (2014, MO14), and Hydrolight Abcase1 Lookup Tables. The differences between the four ORMs in terms of their absorption and backscattering coefficients were evaluated. Preformation of the four ORMs was compared using the NASA bio-Optical Marine Algorithm Dataset and in situ data from the South China Sea. The results showed that preformation of MM01 was the best. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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Review

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28 pages, 11417 KB

Open AccessReview

Identifying Algal Bloom ‘Hotspots’ in Marginal Productive Seas: A Review and Geospatial Analysis

by Maryam R. Al-Shehhi and Yarjan Abdul Samad

Remote Sens. 2022, 14(10), 2457; https://doi.org/10.3390/rs14102457 - 20 May 2022

Cited by 8 | Viewed by 5975

Abstract

Algal blooms in the marginal productive seas of the Indian Ocean are projected to become more prevalent over the coming decades. They reach from lower latitudes up to the coast of the northern Indian Ocean and the populated areas along the Arabian Gulf, Sea of Oman, Arabian Sea, and the Red Sea. Studies that document algal blooms in the Indian Ocean have either focused on individual or regional waters or have been limited by a lack of long-term observations. Herein, we attempt to review the impact of major monsoons on algal blooms in the region and identify the most important oceanic and atmospheric processes that trigger them. The analysis is carried out using a comprehensive dataset collected from many studies focusing on the Indian Ocean. For the first time, we identify ten algal bloom hotspots and identify the primary drivers supporting algal blooms in them. Growth is found to depend on nutrients brought by dust, river runoff, upwelling, mixing, and advection, together with the availability of light, all being modulated by the phase of the monsoon. We also find that sunlight and dust deposition are strong predictors of algal bloom species and are essential for understanding marine biodiversity. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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Other

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13 pages, 5989 KB

Open AccessTechnical Note

Assessment of Empirical and Semi-Analytical Algorithms Using MODIS-Aqua for Representing In-Situ Chromophoric Dissolved Organic Matter (CDOM) in the Bering, Chukchi, and Western Beaufort Seas of the Pacific Arctic Region

by Melishia I. Santiago and Karen E. Frey

Remote Sens. 2021, 13(18), 3673; https://doi.org/10.3390/rs13183673 - 14 Sep 2021

Cited by 9 | Viewed by 3188

Abstract

We analyzed a variety of satellite-based ocean color products derived using MODIS-Aqua to investigate the most accurate empirical and semi-analytical algorithms for representing in-situ chromophoric dissolved organic matter (CDOM) across a large latitudinal transect in the Bering, Chukchi, and western Beaufort Seas of the Pacific Arctic region. In particular, we compared the performance of empirical (CDOM index) and several semi-analytical algorithms (quasi-analytical algorithm (QAA), Carder, Garver-Siegel-Maritorena (GSM), and GSM-A) with field measurements of CDOM absorption (a_CDOM) at 412 nanometers (nm) and 443 nm. These algorithms were compared with in-situ CDOM measurements collected on cruises during July 2011, 2013, 2014, 2015, 2016, and 2017. Our findings show that the QAA a443 and GSM-A a443 algorithms are the most accurate and robust representation of in-situ conditions, and that the GSM-A a443 algorithm is the most accurate algorithm when considering all statistical metrics utilized here. Our further assessments indicate that geographic variables (distance to coast, latitude, and sampling transects) did not obviously relate to algorithm accuracy. In general, none of the algorithms investigated showed a statistically significant agreement with field measurements beyond an approximately ± 60 h offset, likely owing to the highly variable environmental conditions found across the Pacific Arctic region. As such, we suggest that satellite observations of CDOM in these Arctic regions should not be used to represent in-situ conditions beyond a ± 60 h timeframe. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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18 pages, 5954 KB

Open AccessTechnical Note

Analyzing Satellite Ocean Color Match-Up Protocols Using the Satellite Validation Navy Tool (SAVANT) at MOBY and Two AERONET-OC Sites

by Adam Lawson, Jennifer Bowers, Sherwin Ladner, Richard Crout, Christopher Wood, Robert Arnone, Paul Martinolich and David Lewis

Remote Sens. 2021, 13(14), 2673; https://doi.org/10.3390/rs13142673 - 7 Jul 2021

Cited by 7 | Viewed by 3119

Abstract

The satellite validation navy tool (SAVANT) was developed by the Naval Research Laboratory to help facilitate the assessment of the stability and accuracy of ocean color satellites, using numerous ground truth (in situ) platforms around the globe and support methods for match-up protocols. The effects of varying spatial constraints with permissive and strict protocols on match-up uncertainty are evaluated, in an attempt to establish an optimal satellite ocean color calibration and validation (cal/val) match-up protocol. This allows users to evaluate the accuracy of ocean color sensors compared to specific ground truth sites that provide continuous data. Various match-up constraints may be adjusted, allowing for varied evaluations of their effects on match-up data. The results include the following: (a) the difference between aerosol robotic network ocean color (AERONET-OC) and marine optical Buoy (MOBY) evaluations; (b) the differences across the visible spectrum for various water types; (c) spatial differences and the size of satellite area chosen for comparison; and (d) temporal differences in optically complex water. The match-up uncertainty analysis was performed using Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) SNPP data at the AERONET-OC sites and the MOBY site. It was found that the more permissive constraint sets allow for a higher number of match-ups and a more comprehensive representation of the conditions, while the restrictive constraints provide better statistical match-ups between in situ and satellite sensors. Full article

(This article belongs to the Special Issue Bio-Optical Oceanic Remote Sensing)

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