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Moving Forward on Remote Sensing of Sea Surface Salinity
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Moving Forward on Remote Sensing of Sea Surface Salinity

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

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 42027

Special Issue Editor

Dr. Viviane V. Menezes

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Guest Editor
Physical Oceanography Department, Woods Hole Oceanographic Institution, 266 Woods Hole Road, MS 21, Woods Hole, MA 02543, USA
Interests: ocean salinity; Indian Ocean circulation; Antarctic bottom water

Special Issue Information

Dear Colleagues,

It is our pleasure to announce that we are organizing a Special Issue on the Remote Sensing of Sea Surface Salinity (SSS) in the Remote Sensing journal of MDPI. In the last ten years, much has been accomplished through SMOS, Aquarius, and SMAP missions. However, challenges in the remote sensing of sea surface salinity still persist, especially in cold-water, coastal, and continental shelf regions across the globe. In addition, the link between satellite SSS and the hydrological cycle, which inspired the first studies in remote sensing of salinity, has not been fully explored to date in both regional and global scales. In this Special Issue, we welcome papers exploring all these areas in remote sensing of salinity, especially papers focusing on SSS variability in high latitudes.

The topics of interest include but are not limited to:

  • Investigations of SSS variability using satellite(s) and in situ observations in high latitudes, e.g., in the Arctic, Sub-polar North Atlantic, and the Southern Ocean;
  • Evaluation of remote sensing products from SMOS, Aquarius, and SMAP in multiple scales against in situ observations, especially from cutting-edge technologies such as sail-drones;
  • Sea surface salinity studies in coastal and continental shelf areas based on satellite(s) and in situ observations;
  • Synergistic use of satellite SSS, in situ observations, and other satellite-based products to obtain a better understanding of the hydrological cycle (both local and global scales);
  • Effects of rain on satellite salinity retrieval;
  • Improvements of salinity products in cold waters, coastal, and continental shelf regions;
  • New technologies, algorithms, and studies that aim to enhance SSS remote sensing capabilities.

Dr. Viviane V. Menezes
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. 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 2700 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

  • SMAP
  • SMOS
  • Aquarius
  • Artic
  • Southern Ocean
  • Sub-polar North Atlantic
  • cold water
  • coastal region
  • continental shelf

Published Papers (15 papers)

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16 pages, 2691 KiB  
Article
Validating Salinity from SMAP and HYCOM Data with Saildrone Data during EUREC4A-OA/ATOMIC
by Kashawn Hall, Alton Daley, Shanice Whitehall, Sanola Sandiford and Chelle L. Gentemann
Remote Sens. 2022, 14(14), 3375; https://doi.org/10.3390/rs14143375 - 13 Jul 2022
Cited by 2 | Viewed by 1927
Abstract
The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. [...] Read more.
The 2020 ‘Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere’ (EUREC4A-OA) and the ‘Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign’ (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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19 pages, 12659 KiB  
Article
Sea Surface Salinity Variability in the Bering Sea in 2015–2020
by Jian Zhao, Yan Wang, Wenjing Liu, Hongsheng Bi, Edward D. Cokelet, Calvin W. Mordy, Noah Lawrence-Slavas and Christian Meinig
Remote Sens. 2022, 14(3), 758; https://doi.org/10.3390/rs14030758 - 06 Feb 2022
Cited by 6 | Viewed by 2908
Abstract
Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, [...] Read more.
Salinity in the Bering Sea is vital for the physical environment that is tied to the productive ecosystem and the properties of Pacific waters transported to the Arctic Ocean. Its salinity variability reflects many fundamental processes, including sea ice formation/melting and river runoff, but its spatial and temporal characteristics require better documentation. This study utilizes remote sensing products and in situ observations collected by saildrone missions to investigate Sea Surface Salinity (SSS) variability. All Satellite products resolve the large-scale pattern set up by the relatively salty deep basin and the fresh coastal region, but they can be inaccurate near the ice edge and near land. The SSS annual cycle exhibits seasonal maxima in winter to spring, and minima in summer to fall. The amplitude and timing of the seasonal cycle are variable, especially on the eastern Bering Sea shelf. SSS variability recorded by both saildrone, and satellite instruments provide unprecedented insights into short-term oceanic processes including sea ice melting, wind-driven currents during weather events, and river plumes etc. In particular, the Soil Moisture Active Passive (SMAP) satellite demonstrates encouraging skills in capturing the freshening signals induced by spring sea ice melting. The Yukon River plume is another source of intense SSS variability. Surface wind forcing plays an essential role in controlling the horizontal movement of plume water and thereby shaping the SSS seasonal cycle in local regions. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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24 pages, 37681 KiB  
Article
Evaluation of SMOS L4 Sea Surface Salinity Product in the Western Iberian Coast
by Beatriz Biguino, Estrella Olmedo, Afonso Ferreira, Nuno Zacarias, Luísa Lamas, Luciane Favareto, Carla Palma, Carlos Borges, Ana Teles-Machado, Joaquim Dias, Paola Castellanos and Ana C. Brito
Remote Sens. 2022, 14(2), 423; https://doi.org/10.3390/rs14020423 - 17 Jan 2022
Cited by 2 | Viewed by 2037
Abstract
Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years [...] Read more.
Salinity is one of the oldest parameters being measured in oceanography and one of the most important to study in the context of climate change. However, its quantification by satellite remote sensing has been a relatively recent achievement. Currently, after over ten years of data gathering, there are still many challenges in quantifying salinity from space, especially when it is intended for coastal environments study. That is mainly due to the spatial resolution of the available products. Recently, a new higher resolution (5 km) L4 SMOS sea surface salinity (SSS) product was developed by the Barcelona Expert Center (BEC). In this study, the quality of this product was tested along the Western Iberian Coast through its comparison with in situ observations and modelled salinity estimates (CMEMS IBI Ocean Reanalysis system). Moreover, several parameters such as the temperature and depth of in situ measurements were tested to identify the variables or processes that induced higher errors in the product or influenced its performance. Lastly, a seasonal and interannual analysis was conducted considering data between 2011 to 2019 to test the product as a potential tool for long-term studies. The results obtained in the present analysis showed a high potential of using the L4 BEC SSS SMOS product in extended temporal and spatial analyses along the Portuguese coast. A good correlation between the satellite and the in situ datasets was observed, and the satellite dataset showed lower errors in retrieving coastal salinities than the oceanic model. Overall, the distance to the coast and the closest rivers were the factors that most influenced the quality of the product. The present analysis showed that great progress has been made in deriving coastal salinity over the years and that the SMOS SSS product is a valuable contribution to worldwide climatological studies. In addition, these results reinforce the need to continue developing satellite remote sensing products as a global and cost-effective methodology for long-term studies. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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22 pages, 8075 KiB  
Article
Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean
by Sarah B. Hall, Bulusu Subrahmanyam and James H. Morison
Remote Sens. 2022, 14(1), 71; https://doi.org/10.3390/rs14010071 - 24 Dec 2021
Cited by 5 | Viewed by 3259
Abstract
Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region [...] Read more.
Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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21 pages, 5587 KiB  
Article
SMAP Salinity Retrievals near the Sea-Ice Edge Using Multi-Channel AMSR2 Brightness Temperatures
by Thomas Meissner and Andrew Manaster
Remote Sens. 2021, 13(24), 5120; https://doi.org/10.3390/rs13245120 - 16 Dec 2021
Cited by 5 | Viewed by 2455
Abstract
Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval [...] Read more.
Sea-ice contamination in the antenna field of view constitutes a large error source in retrieving sea-surface salinity (SSS) with the spaceborne Soil Moisture Active Passive (SMAP) L-band radiometer. This is a major obstacle in the current NASA/Remote Sensing Systems (RSS) SMAP SSS retrieval algorithm in regards to obtaining accurate SSS measurements in the polar oceans. Our analysis finds a strong correlation between 8-day averaged SMAP L-band brightness temperature (TB) bias and TB measurements from the Advanced Microwave Scanning Radiometer (AMSR2) in the C-through Ka-band frequency range for sea-ice contaminated ocean scenes. We show how this correlation can be employed to develop: (1) a discriminant analysis that is able to reliably flag the SMAP observations for sea-ice contamination and (2) subsequently remove the sea-ice contamination from the SMAP observations, which results in significantly more accurate SMAP SSS retrievals near the sea-ice edge. We provide a case study that evaluates the performance of the proposed sea-ice flagging and correction algorithm. Our method is also able to detect drifting icebergs, which go often undetected in many available standard sea-ice products and thus result in spurious SMAP SSS retrievals. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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15 pages, 5230 KiB  
Article
Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model
by Frederick M. Bingham, Severine Fournier, Susannah Brodnitz, Karly Ulfsax and Hong Zhang
Remote Sens. 2021, 13(15), 2995; https://doi.org/10.3390/rs13152995 - 30 Jul 2021
Cited by 6 | Viewed by 2601
Abstract
Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution [...] Read more.
Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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23 pages, 6686 KiB  
Article
Global Analysis of Coastal Gradients of Sea Surface Salinity
by Alina N. Dossa, Gaël Alory, Alex Costa da Silva, Adeola M. Dahunsi and Arnaud Bertrand
Remote Sens. 2021, 13(13), 2507; https://doi.org/10.3390/rs13132507 - 26 Jun 2021
Cited by 10 | Viewed by 2487
Abstract
Sea surface salinity (SSS) is a key variable for ocean–atmosphere interactions and the water cycle. Due to its climatic importance, increasing efforts have been made for its global in situ observation, and dedicated satellite missions have been launched more recently to allow homogeneous [...] Read more.
Sea surface salinity (SSS) is a key variable for ocean–atmosphere interactions and the water cycle. Due to its climatic importance, increasing efforts have been made for its global in situ observation, and dedicated satellite missions have been launched more recently to allow homogeneous coverage at higher resolution. Cross-shore SSS gradients can bear the signature of different coastal processes such as river plumes, upwelling or boundary currents, as we illustrate in a few regions. However, satellites performances are questionable in coastal regions. Here, we assess the skill of four gridded products derived from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites and the GLORYS global model reanalysis at capturing cross-shore SSS gradients in coastal bands up to 300 km wide. These products are compared with thermosalinography (TSG) measurements, which provide continuous data from the open ocean to the coast along ship tracks. The comparison shows various skills from one product to the other, decreasing as the coast gets closer. The bias in reproducing coastal SSS gradients is unrelated to how the SSS biases evolve with the distance to the coast. Despite limited skill, satellite products generally agree better with collocated TSG data than a global reanalysis and show a large range of coastal SSS gradients with different signs. Moreover, satellites reveal a global dominance of coastal freshening, primarily related to river runoff over shelves. This work shows a great potential of SSS remote sensing to monitor coastal processes, which would, however, require a jump in the resolution of future SSS satellite missions to be fully exploited. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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21 pages, 9387 KiB  
Article
Investigating the Response of Temperature and Salinity in the Agulhas Current Region to ENSO Events
by Corinne B. Trott, Bulusu Subrahmanyam and Caroline E. Washburn
Remote Sens. 2021, 13(9), 1829; https://doi.org/10.3390/rs13091829 - 07 May 2021
Cited by 7 | Viewed by 2525
Abstract
The Agulhas Current is a critical component of global ocean circulation and has been observed to respond to El Niño Southern Oscillation (ENSO) events via its temperature and salinity signatures. In this research, we use sea surface salinity (SSS) from the National Aeronautics [...] Read more.
The Agulhas Current is a critical component of global ocean circulation and has been observed to respond to El Niño Southern Oscillation (ENSO) events via its temperature and salinity signatures. In this research, we use sea surface salinity (SSS) from the National Aeronautics and Space Administration’s (NASA) Soil Moisture Active Passive (SMAP) satellite, sea surface temperature (SST) observations from the Canadian Meteorological Centre (CMC), sea surface height (SSH) anomalies from altimetry, and the Oceanic Niño Index to study the SMAP satellite time period of April 2015 through March 2020 (to observe full years of study). We see warming and high salinities after El Niño, cooling and fresher surface waters after La Niña, and a stronger temperature response than that of salinity. About one year after the 2015 El Niño, there is a warming of the entire region except at the Antarctic Circumpolar Current. About two years after the event, there is an increase in salinity along the eastern coast of Africa and in the Agulhas Current region. About two years after the 2016 and 2018 La Niñas, there is a cooling south of Madagascar and in the Agulhas Current. There are no major changes in salinity seen in the Agulhas Current, but there is a highly saline mass of water west of the Indonesian Throughflow about two years after the La Niña events. Wavelet coherence analysis finds that SSS and ENSO are most strongly correlated a year after the 2015 El Niño and two years after the 2016 La Niña. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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19 pages, 8212 KiB  
Article
Surface Freshwater Fluxes in the Arctic and Subarctic Seas during Contrasting Years of High and Low Summer Sea Ice Extent
by Sarah B. Hall, Bulusu Subrahmanyam, Ebenezer S. Nyadjro and Annette Samuelsen
Remote Sens. 2021, 13(8), 1570; https://doi.org/10.3390/rs13081570 - 18 Apr 2021
Cited by 5 | Viewed by 2683
Abstract
Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea [...] Read more.
Freshwater (FW) flux between the Arctic Ocean and adjacent waterways, predominantly driven by wind and oceanic currents, influences halocline stability and annual sea ice variability which further impacts global circulation and climate. The Arctic recently experienced anomalous years of high and low sea ice extent in the summers of 2013/2014 and 2012/2016, respectively. Here we investigate the interannual variability of oceanic surface FW flux in relation to spatial and temporal variability in sea ice concentration (SIC), sea surface salinity (SSS), and sea surface temperature (SST), focusing on years with summer sea–ice extremes. Our analysis between 2010–2018 illustrate high parameter variability, especially within the Laptev, Kara, and Barents seas, as well as an overall decreasing trend of FW flux through the Fram Strait. We find that in 2012, a maximum average FW flux of 0.32 × 103 ms−1 in October passed over a large portion of the Northeast Atlantic Ocean at 53°N. This study highlights recent changes in the Arctic and Subarctic Seas and the importance of continued monitoring of key variables through remote sensing to understand the dynamics behind these ongoing changes. Observations of FW fluxes through major Arctic routes will be increasingly important as the polar regions become more susceptible to warming, with major impacts on global climate. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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28 pages, 11833 KiB  
Article
Intercomparison of Global Sea Surface Salinity from Multiple Datasets over 2011–2018
by Hao Liu and Zexun Wei
Remote Sens. 2021, 13(4), 811; https://doi.org/10.3390/rs13040811 - 23 Feb 2021
Cited by 11 | Viewed by 2930
Abstract
The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based [...] Read more.
The variability in sea surface salinity (SSS) on different time scales plays an important role in associated oceanic or climate processes. In this study, we compare the SSS on sub-annual, annual, and interannual time scales among ten datasets, including in situ-based and satellite-based SSS products over 2011–2018. Furthermore, the dominant mode on different time scales is compared using the empirical orthogonal function (EOF). Our results show that the largest spread of ten products occurs on the sub-annual time scale. High correlation coefficients (0.6~0.95) are found in the global mean annual and interannual SSSs between individual products and the ensemble mean. Furthermore, this study shows good agreement among the ten datasets in representing the dominant mode of SSS on the annual and interannual time scales. This analysis provides information on the consistency and discrepancy of datasets to guide future use, such as improvements to ocean data assimilation and the quality of satellite-based data. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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25 pages, 13820 KiB  
Article
Sea Surface Salinity Response to Tropical Cyclones Based on Satellite Observations
by Jingru Sun, Gabriel Vecchi and Brian Soden
Remote Sens. 2021, 13(3), 420; https://doi.org/10.3390/rs13030420 - 26 Jan 2021
Cited by 17 | Viewed by 3302
Abstract
Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution [...] Read more.
Multi-year records of satellite remote sensing of sea surface salinity (SSS) provide an opportunity to investigate the climatological characteristics of the SSS response to tropical cyclones (TCs). In this study, the influence of TC winds, rainfall and preexisting ocean stratification on SSS evolution is examined with multiple satellite-based and in-situ data. Global storm-centered composites indicate that TCs act to initially freshen the ocean surface (due to precipitation), and subsequently salinify the surface, largely through vertical ocean processes (mixing and upwelling), although regional hydrography can lead to local departure from this behavior. On average, on the day a TC passes, a strong SSS decrease is observed. The fresh anomaly is subsequently replaced by a net surface salinification, which persists for weeks. This salinification is larger on the right (left)-hand side of the storm motion in the Northern (Southern) Hemisphere, consistent with the location of stronger turbulent mixing. The influence of TC intensity and translation speed on the ocean response is also examined. Despite having greater precipitation, stronger TCs tend to produce longer-lasting, stronger and deeper salinification especially on the right-hand side of the storm motion. Faster moving TCs are found to have slightly weaker freshening with larger area coverage during the passage, but comparable salinification after the passage. The ocean haline response in four basins with different climatological salinity stratification reveals a significant impact of vertical stratification on the salinity response during and after the passage of TCs. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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16 pages, 5729 KiB  
Article
Observations of Mesoscale Eddies in Satellite SSS and Inferred Eddy Salt Transport
by Oleg Melnichenko, Peter Hacker and Vasco Müller
Remote Sens. 2021, 13(2), 315; https://doi.org/10.3390/rs13020315 - 18 Jan 2021
Cited by 11 | Viewed by 3695
Abstract
Observations of sea surface salinity (SSS) from NASA’s Soil Moisture Active-Passive (SMAP) and ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite missions are used to characterize and quantify the contribution of mesoscale eddies to the ocean transport of salt. Given large errors in [...] Read more.
Observations of sea surface salinity (SSS) from NASA’s Soil Moisture Active-Passive (SMAP) and ESA’s Soil Moisture and Ocean Salinity (SMOS) satellite missions are used to characterize and quantify the contribution of mesoscale eddies to the ocean transport of salt. Given large errors in satellite retrievals and, consequently, SSS maps, we evaluate two products from the two missions and also use two different methods to assess the eddy transport of salt. Comparing the two missions, we find that the estimates of the eddy transport of salt agree very well, particularly in the tropics and subtropics. The transport is divergent in the subtropical gyres (eddies pump salt out of the gyres) and convergent in the tropics. The estimates from the two satellites start to differ regionally at higher latitudes, particularly in the Southern Ocean and along the Antarctic Circumpolar Current (ACC), resulting, presumably, from a considerable increase in the level of noise in satellite retrievals (because of poor sensitivity of the satellite radiometer to SSS in cold water), or they can be due to insufficient spatial resolution. Overall, our study demonstrates that the possibility of characterizing and quantifying the eddy transport of salt in the ocean surface mixed layer can rely on the use of satellite observations of SSS. Yet, new technologies are required to improve the resolution capabilities of future satellite missions in order to observe mesoscale and sub-mesoscale variability, improve the signal-to-noise ratio, and extend these capabilities to the polar oceans. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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17 pages, 2711 KiB  
Article
Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations
by Frederick M. Bingham, Susannah Brodnitz and Lisan Yu
Remote Sens. 2021, 13(1), 110; https://doi.org/10.3390/rs13010110 - 31 Dec 2020
Cited by 9 | Viewed by 2925
Abstract
Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS [...] Read more.
Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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23 pages, 8474 KiB  
Article
Variability and Uncertainty of Satellite Sea Surface Salinity in the Subpolar North Atlantic (2010–2019)
by Lisan Yu
Remote Sens. 2020, 12(13), 2092; https://doi.org/10.3390/rs12132092 - 30 Jun 2020
Cited by 8 | Viewed by 2621
Abstract
Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at [...] Read more.
Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at mid-high latitudes has yet to be established. Here, four SSS products derived from two satellite missions were evaluated in the subpolar North Atlantic Ocean in reference to two in situ gridded products. Harmonic analysis of annual and semiannual cycles in in situ products revealed that seasonal variations of SSS are dominated by an annual cycle, with a maximum in March and a minimum in September. The annual amplitudes are larger (>0.3 practical salinity scale (pss)) in the western basin where surface waters are colder and fresher, and weaker (~0.06 pss) in the eastern basin where surface waters are warmer and saltier. Satellite SSS products have difficulty producing the right annual cycle, particularly in the Labrador/Irminger seas where the SSS seasonality is dictated by the influx of Arctic low-salinity waters along the boundary currents. The study also found that there are basin-scale, time-varying drifts in the decade-long SMOS data records, which need to be corrected before the datasets can be used for studying climate variability of SSS. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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13 pages, 2591 KiB  
Letter
Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions
by Frederick M. Bingham and Zhijin Li
Remote Sens. 2020, 12(23), 3996; https://doi.org/10.3390/rs12233996 - 06 Dec 2020
Cited by 4 | Viewed by 1887
Abstract
Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a [...] Read more.
Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall. Full article
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
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