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New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor...
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New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor Fusion, and Hyperspectral Sensing

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Physical Oceanography".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 6773

Special Issue Editor

Dr. Jason Keith Jolliff

E-Mail Website
Guest Editor
Naval Research Laboratory, Stennis Space Center, Hancock County, MS 39529, USA
Interests: ocean color; ocean remote sensing; sensor fusion; hyperspectral sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 1978 launch of the Nimbus 7 spacecraft ushered in a new era of space-borne ocean color observations leading to an improved understanding of our oceans. Technological advancements are once again pushing the scientific horizons on multiple fronts: new sensors on revolutionary small satellite platforms, new methods for data analysis that involve machine learning and artificial intelligence, and hyperspectral remote sensing and imaging systems.

The purpose of this Special Issue is to collect papers that are on the cutting-edge of these advancements and that fall under the collective rubric of ocean color remote sensing. This Special Issue encourages submissions from all aspects, technologies, and methods that involve the detection, analysis, and use of visible-band water radiance from the surface oceans. However, we are particularly encouraging submissions from the following areas:

(1) New sensors and platforms: the microsatellite trend is disrupting the traditionally dedicated ocean color sensor/satellite mission paradigm. Hundreds of low-cost and low-weight orbiting platforms host sensors collecting near-continuous visible-band data from the surface of the oceans. This call encourages submissions that utilize the “smallsat” data to improve scientific exploration and ocean monitoring.

(2) Sensor fusion: we now have a constellation of satellite-based and ocean-viewing radiometers collecting visible-band information in conjunction with smaller satellite platforms. How should these different data streams be blended or combined into new products? Can these data streams be merged with other data types to provide synoptic summary information about the physics, optics, and biogeochemistry of the surface oceans? This call seeks papers that provide new directions for the application and blending of ocean color information into analysis products. 

(3) With the hyperspectral NASA PACE mission on the horizon, there is now a need for new algorithms and new applications for hyperspectral ocean color data. What new techniques for data processing, atmospheric correction, and inversion algorithms are poised to take advantage of the looming wealth of spectral information?

(4) Novel Applications: Data Assimilation—With multiple ocean color sensors providing daily global coverage—how can these data be used to improve ocean models or coupled biogeochemical ocean models?

(5) Pushing the resolution envelope—we encourage the submission of papers that exploit higher spatial and temporal resolutions to study and monitor the oceans at new time/space scales and examine new phenomena.

(6) Novel Applications—what is next? This call is not limited to the above bulleted points. Any paper that provides a novel method or application in the general field of ocean color remote sensing is strongly encouraged.

Dr. Jason Keith Jolliff
Guest Editor

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. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ocean color
  • Ocean remote sensing
  • Sensor fusion
  • Hyperspectral sensing

Published Papers (3 papers)

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Research

22 pages, 9639 KiB  
Article
Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data
by Sean McCarthy, Summer Crawford, Christopher Wood, Mark D. Lewis, Jason K. Jolliff, Paul Martinolich, Sherwin Ladner, Adam Lawson and Marcos Montes
J. Mar. Sci. Eng. 2023, 11(3), 660; https://doi.org/10.3390/jmse11030660 - 21 Mar 2023
Cited by 4 | Viewed by 1688
Abstract
Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic [...] Read more.
Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [Lw(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between Lw and top-of-atmosphere (Lt)/Rayleigh (Lr) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams. Full article
(This article belongs to the Special Issue New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor Fusion, and Hyperspectral Sensing)
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20 pages, 2678 KiB  
Article
Evaluating the Efficacy of Five Chlorophyll-a Algorithms in Chesapeake Bay (USA) for Operational Monitoring and Assessment
by Timothy T. Wynne, Michelle C. Tomlinson, Travis O. Briggs, Sachidananda Mishra, Andrew Meredith, Ronald L. Vogel and Richard P. Stumpf
J. Mar. Sci. Eng. 2022, 10(8), 1104; https://doi.org/10.3390/jmse10081104 - 12 Aug 2022
Cited by 4 | Viewed by 1832
Abstract
This manuscript describes methods for evaluating the efficacy of five satellite-based Chlorophyll-a algorithms in Chesapeake Bay, spanning three separate sensors: Ocean Land Color Imager (OLCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and MODerate Resolution Imaging Spectroradiometer (MODIS). The algorithms were compared using [...] Read more.
This manuscript describes methods for evaluating the efficacy of five satellite-based Chlorophyll-a algorithms in Chesapeake Bay, spanning three separate sensors: Ocean Land Color Imager (OLCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and MODerate Resolution Imaging Spectroradiometer (MODIS). The algorithms were compared using in situ Chlorophyll-a measurements from 38 separate stations, provided through the Chesapeake Bay Program (CBP). These stations span nearly the entire 300 km length of the optically complex Chesapeake Bay, the largest estuary in the United States. Overall accuracy was examined for the entire dataset, in addition to assessing the differences related to the distance from the turbidity maximum to the north by grouping the results into the upper bay, middle bay, or lower bay. The mean bias and the Mean Absolute Error (MAE) as well as the median bias and Median Absolute Error (MedAE) were conducted for comparison. A two-band algorithm, that is based on the red-edge portion of the electromagnetic spectrum (RE10), when applied to OLCI imagery, exhibited the lowest overall MedAE of 36% at all stations. As a result, it is recommended that the RE10 algorithm be applied to OLCI and provided as an operational product through NOAA’s CoastWatch program. The paper will conclude with results from a brief climatological analysis using the OLCI RE10 algorithm. Full article
(This article belongs to the Special Issue New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor Fusion, and Hyperspectral Sensing)
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22 pages, 15864 KiB  
Article
Seasonal Evolution of Chlorophyll-a in the North Indian Ocean Associated with the Indian Ocean Dipole and Two Types of El Niño Events
by Zi Yin, Qing Dong, Kunsheng Xiang and Min Bian
J. Mar. Sci. Eng. 2022, 10(7), 997; https://doi.org/10.3390/jmse10070997 - 21 Jul 2022
Viewed by 1362
Abstract
To investigate the main modes of interannual variation of chlorophyll-a (Chla) with seasonal evolution and its variation cycle in the North Indian Ocean based on satellite-derived products during 1998–2016, a season-reliant empirical orthogonal function (S-EOF) analysis and power spectrum analysis based on Fourier [...] Read more.
To investigate the main modes of interannual variation of chlorophyll-a (Chla) with seasonal evolution and its variation cycle in the North Indian Ocean based on satellite-derived products during 1998–2016, a season-reliant empirical orthogonal function (S-EOF) analysis and power spectrum analysis based on Fourier transform are applied in the study. The first three dominate modes reveal distinct Chla variability, as the S-EOF1 features by one dipole pattern have a negative anomaly in the central western Indian Ocean and a positive anomaly off the Java–Sumatra coasts, which is mainly synchronously associated with the climate indices of the positive Indian Ocean dipole (IOD) and eastern Pacific El Nino (EP-El Niño). The S-EOF2 indicates a tripolar structure with positive anomalies located in the central Indian Ocean surrounded by two negative anomalies, which is one year behind a positive IOD and EP-El Niño event. The S-EOF3 exhibits a different dipole distribution, with a positive anomaly in the central west and a negative anomaly in the southeast, synchronized or lagging behind the central Pacific El Nino (CP-El Niño). Moreover, regarding the correlation between the main modes of interannual variation and the IOD and El Nino events, the dynamic parameters (such as SST, SLA, rain, and wind) of the tropical Indo-Pacific Ocean are discussed using time-delay correlation and linear regression analysis to explain the key factors and possible influencing mechanism of the joint seasonal and interannual variations of Chla in the northern Indian Ocean. Full article
(This article belongs to the Special Issue New Frontiers in Ocean Color Remote Sensing: Novel Applications, Sensor Fusion, and Hyperspectral Sensing)
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