Special Issue "Feature Paper Special Issue on Ocean Remote Sensing"

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

Deadline for manuscript submissions: closed (28 February 2021).

Special Issue Editors

Dr. Weimin Huang
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: ocean remote sensing; high frequency surface-wave radar (HFSWR); microwave nautical radar; ocean observation using global navigation satellite systems (GNSS)
Special Issues and Collections in MDPI journals
Prof. Dr. Deepak R. Mishra
E-Mail Website
Guest Editor
Institutional information: Department of Geography, University of Georgia, 210 Field Street, Rm 212B, Athens, GA 30602, USA
Interests: water quality (inland waters, estuaries, coastal, and open ocean waters); wetlands health, productivity, and carbon sequestration; benthic habitat mapping, cyber-innovated environmental sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Ocean is the major reservoir of water, heat, and greenhouse gases on Earth. Remote sensing has been a key technology in ocean observation. Ocean remote sensing uses modern instruments including satellite, radar, as well as altimetry to study important ocean phenomena and processes.

We invite you to submit reviews or research articles to this Special Issue in order to improve the current knowledge on ocean remote sensing. Papers addressing ocean information retrieval methods, remote sensing data validation, calibration, and applications based on remote sensing data are welcome.

The applications or technologies in your work should be novel and should bring new information to this area.

Dr. Weimin Huang
Prof. Dr. Deepak R. Mishra
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 2400 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

  • Remote sensing of ocean color
  • Remote sensing of sea surface temperature and salinity
  • Remote sensing of sea surface winds, waves, currents, and sea ice
  • Remote detection of hard targets (ships, oil rigs, etc.) and oil spill/seep
  • Remote sensing image segmentation and classification in coastal environment
  • Radiometer, scatterometer, altimeter, synthetic aperture radar applications in oceanography
  • LIDAR remote sensing
  • Data fusion and assimilation
  • Dedicated ocean satellite missions
  • Operational oceanography
  • Physical, biological, chemical, and geological oceanography studies using remote sensing data
  • Marine meteorological studies using remote sensing
  • Synergy of remote sensing and modeling techniques for ocean studies

Published Papers (12 papers)

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Article
Cross-Domain Submesoscale Eddy Detection Neural Network for HF Radar
Remote Sens. 2021, 13(13), 2441; https://doi.org/10.3390/rs13132441 - 22 Jun 2021
Viewed by 356
Abstract
With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the [...] Read more.
With the rapid development of deep learning, the neural network becomes an efficient approach for eddy detection. However, previous work employs a traditional neural network with a focus on improving the detecting accuracy only using limited data under a single scenario. Meanwhile, the experience of detecting eddies from one experiment is not directly inherited from the detection model for other experiments. Therefore, a cross-domain submesoscale eddy detection neural network (CDEDNet) based on the high-frequency radar (HFR) data of the Nansan and Xuwen region is proposed in this paper. Firstly, a fundamental deep eddy detection architecture CDEDNet-0 is constructed with a fully convolutional network (FCN). Secondly, for solving the problem of insufficient labeled eddy data, an instance-based domain adaption method is adopted in CDEDNet-1 to increase training samples. Thirdly, for tackling the problem of unable to inherit previous detection experience, parameter-based transfer learning is incorporated in CDEDNet-2 for multi-scene eddy detection. The experiment results demonstrate CDEDNet-1 and CDEDNet-2 perform better than CDEDNet-0 in terms of accuracy. Meanwhile, eddy characteristics including eddy type, radius, occurring time, merger, and dynamic trajectory are analyzed for the Nansan and Xuwen regions. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Effect of 6-DOF Oscillation of Ship Target on SAR Imaging
Remote Sens. 2021, 13(9), 1821; https://doi.org/10.3390/rs13091821 - 07 May 2021
Cited by 1 | Viewed by 357
Abstract
Ship targets are high-value military and civilian targets with broad application prospects. However, the precise focusing of ships is still a difficult issue because of their complicated six-degree-of-freedom motions on the sea surface. This paper focused on investigating the effect of ship six-degree-of-freedom [...] Read more.
Ship targets are high-value military and civilian targets with broad application prospects. However, the precise focusing of ships is still a difficult issue because of their complicated six-degree-of-freedom motions on the sea surface. This paper focused on investigating the effect of ship six-degree-of-freedom oscillation on Synthetic Aperture Radar imaging. Firstly, based on the six-degree-of-freedom motions, the accurate range models for ship linear oscillation and angular oscillation were built, and the superiority was verified by comparing them with the models described in published literature. Secondly, we used the Taylor formula and Bessel function to expand the phase error introduced by ship oscillation, then their effects on imaging were further analyzed. Finally, based on the measured ship attitude data, we generated the semi-physical echoes of the oscillatory ship to validate the analysis throughout this article. Based on the proposed range model, we also made some tentative on the phase compensation method by fitting ship attitude angles with multiple sinusoidal functions. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Joint Ship Detection Based on Time-Frequency Domain and CFAR Methods with HF Radar
Remote Sens. 2021, 13(8), 1548; https://doi.org/10.3390/rs13081548 - 16 Apr 2021
Viewed by 394
Abstract
Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection [...] Read more.
Compact high-frequency surface wave radar (HFSWR) plays a critical role in ship surveillance. Due to the wide antenna beam-width and low spatial gain, traditional constant false alarm rate (CFAR) detectors often induce a low detection probability. To solve this problem, a joint detection algorithm based on time-frequency (TF) analysis and the CFAR method is proposed in this paper. After the TF ridge extraction, CFAR detection is performed to test each sample of the ridges, and a binary integration is run to determine whether the entire TF ridge is of a ship. To verify the effectiveness of the proposed algorithm, experimental data collected by the Ocean State Monitoring and Analyzing Radar, type SD (OSMAR-SD) were used, with the ship records from an automatic identification system (AIS) used as ground truth data. The processing results showed that the joint TF-CFAR method outperformed CFAR in detecting non-stationary and weak signals and those within the first-order sea clutters, whereas CFAR outperformed TF-CFAR in identifying multiple signals with similar frequencies. Notably, the intersection of the matched detection sets by TF-CFAR and CFAR alone was not immense, which takes up approximately 68% of the matched number by CFAR and 25% of that by TF-CFAR; however, the number in the union detection sets was much (>30%) greater than the result of either method. Therefore, joint detection with TF-CFAR and CFAR can further increase the detection probability and greatly improve detection performance under complicated situations, such as non-stationarity, low signal-to-noise ratio (SNR), and within the first-order sea clutters. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Communication
Indian Ocean Crossing Swells: New Insights from “Fireworks” Perspective Using Envisat Advanced Synthetic Aperture Radar
Remote Sens. 2021, 13(4), 670; https://doi.org/10.3390/rs13040670 - 12 Feb 2021
Cited by 1 | Viewed by 678
Abstract
Synthetic Aperture Radar (SAR) in wave mode is a powerful sensor for monitoring the swells propagating across ocean basins. Here, we investigate crossing swells in the Indian Ocean using 10-years Envisat SAR wave mode archive spanning from December 2003 to April 2012. Taking [...] Read more.
Synthetic Aperture Radar (SAR) in wave mode is a powerful sensor for monitoring the swells propagating across ocean basins. Here, we investigate crossing swells in the Indian Ocean using 10-years Envisat SAR wave mode archive spanning from December 2003 to April 2012. Taking the benefit of the unique “fireworks” analysis on SAR observations, we reconstruct the origins and propagating routes that are associated with crossing swell pools in the Indian Ocean. Besides, three different crossing swell mechanisms are discriminated from space by the comparative analysis between results from “fireworks” and original SAR data: (1) in the mid-ocean basin of the Indian Ocean, two remote southern swells form the crossing swell; (2) wave-current interaction; and, (3) co-existence of remote Southern swell and shamal swell contribute to the crossing swells in the Agulhas Current region and the Arabian Sea. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Improving the Retrieval of Carbon-Based Phytoplankton Biomass from Satellite Ocean Colour Observations
Remote Sens. 2020, 12(21), 3640; https://doi.org/10.3390/rs12213640 - 06 Nov 2020
Cited by 1 | Viewed by 748
Abstract
Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine [...] Read more.
Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-a concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (Cphyto). Here, we developed an algorithm to quantify Cphyto from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the Cphyto algorithm; (ii) the refined Cphyto algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in Cphyto across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano- and micro-phytoplankton size classes. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Cross-Sensor Quality Assurance for Marine Observatories
Remote Sens. 2020, 12(21), 3470; https://doi.org/10.3390/rs12213470 - 22 Oct 2020
Viewed by 577
Abstract
Measuring and forecasting changes in coastal and deep-water ecosystems and climates requires sustained long-term measurements from marine observation systems. One of the key considerations in analyzing data from marine observatories is quality assurance (QA). The data acquired by these infrastructures accumulates into Giga [...] Read more.
Measuring and forecasting changes in coastal and deep-water ecosystems and climates requires sustained long-term measurements from marine observation systems. One of the key considerations in analyzing data from marine observatories is quality assurance (QA). The data acquired by these infrastructures accumulates into Giga and Terabytes per year, necessitating an accurate automatic identification of false samples. A particular challenge in the QA of oceanographic datasets is the avoidance of disqualification of data samples that, while appearing as outliers, actually represent real short-term phenomena, that are of importance. In this paper, we present a novel cross-sensor QA approach that validates the disqualification decision of a data sample from an examined dataset by comparing it to samples from related datasets. This group of related datasets is chosen so as to reflect upon the same oceanographic phenomena that enable some prediction of the examined dataset. In our approach, a disqualification is validated if the detected anomaly is present only in the examined dataset, but not in its related datasets. Results for a surface water temperature dataset recorded by our Texas A&M—Haifa Eastern Mediterranean Marine Observatory (THEMO)—over a period of 7 months, show an improved trade-off between accurate and false disqualification rates when compared to two standard benchmark schemes. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
The Traveling Wave Loop Antenna: A Terminated Wire Loop Aerial for Directional High-Frequency Ocean RADAR Transmission
Remote Sens. 2020, 12(17), 2800; https://doi.org/10.3390/rs12172800 - 29 Aug 2020
Viewed by 1177
Abstract
In this paper we document the design, development, results, performance and field applications of a compact directive transmit antenna for the long-range High Frequency ocean RADAR (HFR) systems operating in the International Telecommunication Union (ITU) designated 4MHz and 5MHz radiodetermination bands. The antenna design is based on the combination of the concepts of an electrically small loop with that of travelling wave antenna. This has the effect of inducing a radiated wave predominantly in a direction opposed to that of energy flow on the antenna structures. We demonstrate here that travelling wave design allows for a more compact antenna than other directive options, it has straightforward feed-point matching arrangements, and a flat frequency and phase response over an entire radiodetermination band. In situ measurements of the antenna radiation pattern, obtained with the aid of a drone, correlate well with those obtained from simulations, and show between 8dB and 30dB front-to-back suppression, with a 3dB beam width in the forward lobe of 100 or more. The broad-beam radiation pattern ensures proper illumination over the ocean and the significant front-to-back suppression guarantees reduced interference to terrestrial services. The proposed antenna design is compact and straight forward and can be easily deployed by minimal modifications of an existing transmission antenna. The design may be readily adapted to different environments due to the relative insensitivity of its radiation pattern and frequency response to geometric detail. The only downside to these antennas is their relatively low radiation efficiency which, however, may easily be compensated for by the available power output of a typical HFR transmitter. Antennas based on this design are currently deployed at the SeaSonde HFR sites in New South Wales, Australia, with operational ranges up to 200 km offshore despite their low radiating efficiency and the extremely low output power in use at these installations. Due to their directional pattern, it is also planned to test these antennas in phased-array Wellen RADAR (WERA) systems in both the standard receive arrays: where in-band radio frequency noise of terrestrial origin is impacting on data quality, and in the transmit array: to possibly simplify splitting, phasing and tuning requirements. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Validation and Evaluation of a Ship Echo-Based Array Phase Manifold Calibration Method for HF Surface Wave Radar DOA Estimation and Current Measurement
Remote Sens. 2020, 12(17), 2761; https://doi.org/10.3390/rs12172761 - 26 Aug 2020
Viewed by 865
Abstract
Shore-based phased-array HF radars have been widely used for remotely sensing ocean surface current, wave, and wind around the world. However, phase uncertainties, especially phase distortions, in receiving elements significantly degrade the performance of beam forming and direction-of-arrival (DOA) estimation for phased-array HF [...] Read more.
Shore-based phased-array HF radars have been widely used for remotely sensing ocean surface current, wave, and wind around the world. However, phase uncertainties, especially phase distortions, in receiving elements significantly degrade the performance of beam forming and direction-of-arrival (DOA) estimation for phased-array HF radar. To address this problem, the conventional array signal model is modified by adding a direction-based phase error matrix. Subsequently, an array phase manifold calibration method using antenna responses of incoming ship echoes is proposed. Later, an assessment on the proposed array calibration method is made based on the DOA estimations and current measurements that are obtained from the datasets that were collected with a multi-frequency HF (MHF) radar. MHF radar-estimated DOAs using three calibration strategies are compared with the ship directions that are provided by an Automatic Identification System (AIS). Additionally, comparisons between the MHF radar-derived currents while using three calibration strategies and Acoustic Doppler Current Profilers (ADCP)-measured currents are made. The results indicate that the proposed array calibration method is effective in DOA estimation and current measurement for phased-array HF radars, especially in the phase distortion situation. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Remotely Sensing the Source and Transport of Marine Plastic Debris in Bay Islands of Honduras (Caribbean Sea)
Remote Sens. 2020, 12(11), 1727; https://doi.org/10.3390/rs12111727 - 27 May 2020
Cited by 10 | Viewed by 2279
Abstract
Plastic debris in the global ocean is considered an important issue with severe implications for human health and marine ecosystems. Here, we exploited high-resolution multispectral satellite observations over the Bay Islands and Gulf of Honduras, for the period 2014-2019, to investigate the capability [...] Read more.
Plastic debris in the global ocean is considered an important issue with severe implications for human health and marine ecosystems. Here, we exploited high-resolution multispectral satellite observations over the Bay Islands and Gulf of Honduras, for the period 2014-2019, to investigate the capability of satellite sensors in detecting marine plastic debris. We verified findings with in situ data, recorded the spectral characteristics of floating plastic litter, and identified plastic debris trajectories and sources. The results showed that plastic debris originating from Guatemala’s and Honduras’ rivers (such as Motagua, Ulua, Cangrejal, Tinto and Aguan) ends up in the Caribbean Sea, mainly during the period of August to March, which includes the main rainfall season. The detected spatial trajectories indicated that floating plastic debris travels with an average speed of 6 km d−1, following primarily a southwest (SW) to northeast (NE) direction, driven by the prevailing sea surface currents. Based on several satellite observations, there is no indication of a specific accumulation point, since plastic debris is dispersed by the dynamic circulation in the broader region. Our findings provide evidence that satellite remote sensing is a valuable, cost-effective tool for monitoring the sources and pathways of plastic debris in marine ecosystems, and thus could eventually support management strategies in the global ocean. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Article
Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades
Remote Sens. 2020, 12(5), 826; https://doi.org/10.3390/rs12050826 - 03 Mar 2020
Cited by 7 | Viewed by 265727
Abstract
Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of [...] Read more.
Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr 1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Technical Note
Comparison of a Smartfin with an Infrared Sea Surface Temperature Radiometer in the Atlantic Ocean
Remote Sens. 2021, 13(5), 841; https://doi.org/10.3390/rs13050841 - 24 Feb 2021
Cited by 1 | Viewed by 1262
Abstract
The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal [...] Read more.
The accuracy and precision of satellite sea surface temperature (SST) products in nearshore coastal waters are not well known, owing to a lack of in-situ data available for validation. It has been suggested that recreational watersports enthusiasts, who immerse themselves in nearshore coastal waters, be used as a platform to improve sampling and fill this gap. One tool that has been used worldwide by surfers is the Smartfin, which contains a temperature sensor integrated into a surfboard fin. If tools such as the Smartfin are to be considered for satellite validation work, they must be carefully evaluated against state-of-the-art techniques to quantify data quality. In this study, we developed a Simple Oceanographic floating Device (SOD), designed to float on the ocean surface, and deployed it during the 28th Atlantic Meridional Transect (AMT28) research cruise (September and October 2018). We attached a Smartfin to the underside of the SOD, which measured temperature at a depth of ∼0.1 m, in a manner consistent with how it collects data on a surfboard. Additional temperature sensors (an iButton and a TidbiT v2), shaded and positioned a depth of ∼1 m, were also attached to the SOD at some of the stations. Four laboratory comparisons of the SOD sensors (Smartfin, iButton and TidbiT v2) with an accurate temperature probe (±0.0043 K over a range of 273.15 to 323.15 K) were also conducted during the AMT28 voyage, over a temperature range of 290–309 K in a recirculating water bath. Mean differences (δ), referenced to the temperature probe, were removed from the iButton (δ=0.292 K) and a TidbiT v2 sensors (δ=0.089 K), but not from the Smartfin, as it was found to be in excellent agreement with the temperature probe (δ=0.005 K). The SOD was deployed for 20 min periods at 62 stations (predawn and noon) spanning 100 degrees latitude and a gradient in SST of 19 K. Simultaneous measurements of skin SST were collected using an Infrared Sea surface temperature Autonomous Radiometer (ISAR), a state-of-the-art instrument used for satellite validation. Additionally, we extracted simultaneous SST measurements, collected at slightly different depths, from an underway conductivity, temperature and depth (CTD) system. Over all 62 stations, the mean difference (δ) and mean absolute difference (ϵ) between Smartfin and the underway CTD were −0.01 and 0.06 K respectively (similar results obtained from comparisons between Smartfin and iButton and Smartfin and TidbiT v2), and the δ and ϵ between Smartfin and ISAR were 0.09 and 0.12 K respectively. In both comparisons, statistics varied between noon and predawn stations, with differences related to environmental variability (wind speed and sea-air temperature differences) and depth of sampling. Our results add confidence to the use of Smartfin as a citizen science tool for evaluating satellite SST data, and data collected using the SOD and ISAR were shown to be useful for quantifying near-surface temperature gradients. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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Technical Note
Evaluation of the Significant Wave Height Data Quality for the Sentinel-3 Synthetic Aperture Radar Altimeter
Remote Sens. 2020, 12(18), 3107; https://doi.org/10.3390/rs12183107 - 22 Sep 2020
Viewed by 1016
Abstract
Synthetic aperture radar (SAR) altimeters represent a new method of microwave remote sensing for ocean wave observations. The adoption of SAR technology in the azimuthal direction has the advantage of a high resolution. The Sentinel-3 altimeter is the first radar altimeter to acquire [...] Read more.
Synthetic aperture radar (SAR) altimeters represent a new method of microwave remote sensing for ocean wave observations. The adoption of SAR technology in the azimuthal direction has the advantage of a high resolution. The Sentinel-3 altimeter is the first radar altimeter to acquire global observations in SAR mode; hence, the data quality needs to be assessed before extensively applying these data. The European Space Agency (ESA) evaluates the Sentinel-3 accuracy on a global scale but has yet to perform a detailed analysis in terms of different offshore distances and different water depths. In this paper, Sentinel-3 and Jason-2 significant wave height (SWH) data are matched in both time and space with buoy data from the United States East and West Coasts and the Central Pacific Ocean. The Sentinel-3 SWH data quality is evaluated according to different offshore distances and water depths in comparison with Jason-2 SWH data. In areas more than 50 km offshore, the Sentinel-3 SWH accuracy is generally high and less affected by the water depth and sea conditions (root-mean-square error of 0.28 m and correlation coefficient of 0.98); in areas less than 50 km offshore, the SWH data accuracy is slightly affected by water depth and sea conditions (especially the former). Compared with Jason-2, the observation ability of the Sentinel-3 altimeter in nearshore areas with water depths of 0 m-500 m is greatly improved, but in some deep water areas with stable sea conditions, the Jason-2 SWH data accuracy is higher than that of Sentinel-3. This work provides a reference for the refined application of Sentinel-3 SWH data in offshore deep water areas and nearshore shallow water areas. Full article
(This article belongs to the Special Issue Feature Paper Special Issue on Ocean Remote Sensing)
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