Special Issue "Satellite Derived Global Ocean Product Validation/Evaluation"

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

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Dr. SeungHyun Son
E-Mail Website
Guest Editor
CIRA, Colorado State University & NOAA/NESDIS/STAR, College Park, MD 20740, USA
Interests: remote sensing; ocean color; bio-optical algorithms; water quality; phytoplankton productivity; human/climate-induced changes in marine ecosystem
Prof. Dr. Trevor Platt
E-Mail Website
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 and Collections in MDPI journals
Dr. Shubha Sathyendranath
E-Mail Website
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 and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean satellite instruments provide short-term to long-term (hourly to decadal) observations of physical and biogeochemical phenomena and properties in the global ocean at high spatial resolution. Satellite-measured ocean products including sea surface temperature, ocean colour, sea surface salinity, sea surface height, sea surface winds, and sea ice are important data not only for near-real-time ocean monitoring but also for climate data records (CDR) to investigate changes in the ocean environment and manage marine resources for economic, social and environmental benefits. Ocean-observing satellite sensors have been launched recently by international space agencies including NASA, NOAA, ESA and JAXA (e.g., Aquarius, Advanced Microwave Scanning Radiometer 2 (AMSR2), Jason-3, Ocean and Land Colour Instrument (OLCI), Soil Moisture Ocean Salinity (SMOS), Sea and Land Surface Temperature Radiometer (SLSTR), Second Generation Global Imager (SGLI), and Visible Infrared Imaging Radiometer Suite (VIIRS)) and operationally measure the various physical, biological, and biogeochemical variables in the ocean. Validation/evaluation efforts and uncertainty assessments are crucial to providing more accurate satellite-derived ocean products. Validation of the satellite products requires a combination of ground field measurements, instrumented surface sites, inter-satellite comparisons, and research and modeling efforts with robust methodologies.

In this Special Issue, we encourage contributions including, but not limited to, the validation/evaluation of the oceanic radiometric, geophysical and biogeochemical retrievals from various ocean satellite instruments, inter-sensor bias correction, formal error analysis of satellite-observation systems, stability of satellite data and inter-comparison and assimilation of ocean products from multiple sensors.

Dr. SeungHyun Son
Dr. Trevor Platt
Dr. Shubha Sathyendranath
Guest Editors

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 papers will be 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. Remote Sensing is an international peer-reviewed open access semimonthly 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 1800 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

  • Satellite Remote Sensing
  • Ocean Colour
  • Sea Ice
  • Sea Surface Temperature
  • Sea Surface Height
  • Sea Surface Salinity
  • Validation/Evaluation
  • End-to-end error characterisation
  • Inter-sensor bias correction
  • Stability of satellite data

Published Papers (4 papers)

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Research

Open AccessArticle
Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans
Remote Sens. 2019, 11(22), 2609; https://doi.org/10.3390/rs11222609 - 07 Nov 2019
Abstract
Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean [...] Read more.
Remote-sensing reflectance data collected by ocean colour satellites are processed using bio-optical algorithms to retrieve biogeochemical properties of the ocean. One such important property is the concentration of chlorophyll-a, an indicator of phytoplankton biomass that serves a multitude of purposes in various ocean science studies. Here, the performance of two generic chlorophyll-a algorithms (i.e., a band ratio one, Ocean Colour X (OCx), and a semi-analytical one, Garver–Siegel Maritorena (GSM)) was assessed against two large in situ datasets of chlorophyll-a concentration collected between 1999 and 2016 in the Northeast Pacific (NEP) and Northwest Atlantic (NWA) for three ocean colour sensors: Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). In addition, new regionally-tuned versions of these two algorithms are presented, which reduced the mean error (mg m−3) of chlorophyll-a concentration modelled by OCx in the NWA from −0.40, −0.58 and −0.45 to 0.037, −0.087 and −0.018 for MODIS, SeaWiFS, and VIIRS respectively, and −0.34 and −0.36 to −0.0055 and −0.17 for SeaWiFS and VIIRS in the NEP. An analysis of the uncertainties in chlorophyll-a concentration retrieval showed a strong seasonal pattern in the NWA, which could be attributed to changes in phytoplankton community composition, but no long-term trends were found for all sensors and regions. It was also found that removing the 443 nm waveband for the OCx algorithms significantly improved the results in the NWA. Overall, GSM performed better than the OCx algorithms in both regions for all three sensors but generated fewer chlorophyll-a retrievals than the OCx algorithms. Full article
(This article belongs to the Special Issue Satellite Derived Global Ocean Product Validation/Evaluation)
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Open AccessArticle
Evaluation of Chlorophyll-a and POC MODIS Aqua Products in the Southern Ocean
Remote Sens. 2019, 11(15), 1793; https://doi.org/10.3390/rs11151793 - 31 Jul 2019
Abstract
The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua [...] Read more.
The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km × 1 km resolution) chlorophyll-a (C S a t ) and Particulate Organic Carbon (POC s a t ) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (C i n s i t u ) from both High Performance Liquid Chromatography (C H P L C ) and fluorometry (C F l u o ). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of ±12 h and a mean satellite chlorophyll from a 5 × 5 pixel box centered on the in situ location, the median C S a t :C i n s i t u ratios were 0.89 (N = 46) and 0.49 (N = 73) for C H P L C and C F l u o respectively. Differences between C H P L C and C F l u o were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of ∼0.13 mg m 3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POC i n s i t u measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products. Full article
(This article belongs to the Special Issue Satellite Derived Global Ocean Product Validation/Evaluation)
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Open AccessArticle
The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers
Remote Sens. 2019, 11(5), 521; https://doi.org/10.3390/rs11050521 - 04 Mar 2019
Abstract
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, [...] Read more.
Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns. Full article
(This article belongs to the Special Issue Satellite Derived Global Ocean Product Validation/Evaluation)
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Open AccessArticle
Ice Surface Temperature Retrieval from a Single Satellite Imager Band
Remote Sens. 2018, 10(12), 1909; https://doi.org/10.3390/rs10121909 - 29 Nov 2018
Cited by 2
Abstract
Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. [...] Read more.
Current methods for estimating the surface temperature of sea and lake ice—the ice surface temperature (IST)—utilize two satellite imager thermal bands (11 and 12 μm) at moderate spatial resolution. These “split-window” or dual-band methods have been shown to have low biases and uncertainties. A single-band algorithm would be useful for satellite imagers that have only the 11 μm band at high resolution, such as the Visible Infrared Imaging Radiometer Suite (VIIRS), or that do not have a fully functional 12 μm band, such as the Thermal Infrared Sensor onboard the Landsat 8. This study presents a method for single-band IST retrievals, and validation of the retrievals using IST measurements from an airborne infrared radiation pyrometer during the NASA IceBridge campaign in the Arctic. Results show that IST with a single thermal band from the VIIRS has comparable performance to IST with the VIIRS dual-band (split-window) method, with a bias of 0.22 K and root-mean-square error of 1.03 K. Full article
(This article belongs to the Special Issue Satellite Derived Global Ocean Product Validation/Evaluation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Evaluation of Various Satellite Ocean Color Match-Up Protocols and Their Effect On Uncertainty Using Satellite and Ground Truth AeroNET-OC Radiance
Author: Lawson Adam
Affiliation: Naval Research Laboratory, Code 7331, Stennis Space Center, MS 39529, USA
Abstract: The SAtellite VAlidation Navy Tool (SAVANT) was developed by the Navy to help facilitate the assessment of the stability and accuracy of ocean color satellites using numerous ground truth (insitu) platform and buoy stations positioned around the globe and support methods for match-up protocols. SAVANT houses an extensive match-up data set of coincident global ocean color satellite and ground truth spectral water-leaving radiance data allowing uncertainty evaluation. We evaluate the effect of varying spatial, temporal, and geometric constraints and permissive, moderate, and strict constraint sets on match-up uncertainty in an attempt to establish an optimal satellite ocean color cal/val match-up protocol.

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