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Special Issue "Sea Surface Salinity 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 (30 November 2018).

Special Issue Editors

Guest Editor
Dr. Emmanuel P. Dinnat

NASA - Goddard Space Flight Center​, Greenbelt, MD 20785, USA CEESMO, Chapman University, Orange, CA 92866, USA
Website | E-Mail
Phone: (+1) 301-614-6871
Interests: radiative transfer modeling and retrieval algorithms; combined active and passive microwave remote sensing for monitoring of sea surface salinity (SSS); surface roughness; ocean and cryosphere interactions; soil moisture
Guest Editor
Dr. Xiaobin Yin

Marine Department, Beijing Piesat Information Technology Co. Ltd A, Beijing, ChinaAdjunct professor at Hohai university, Nanjing, China
Website | E-Mail
Interests: remote sensing algorithms of SSS, SST, wind vector, rain rate, et al. from space; ocean surface emission modeling at microwave frequencies; validation of polarimetric radiometer data; ocean response to storms, wave-current interactions

Special Issue Information

Dear Colleagues,

Sea Surface salinity (SSS) is an essential climate variable. It is a key component of the water cycle, as a tracer of precipitation and evaporation, river outflow and ice melt/freeze. It is a key driver of the oceanic circulation through its role on the ocean density. It is also a critical parameter for understanding the variability of the ocean carbon fluxes, providing information on water masses and of their chemical properties. SSS in the open ocean has been monitored from space since 2010 by the ESA's SMOS, NASA/CONAE's Aquarius/SAC-D missions, and more recently by the NASA's SMAP mission.

The purpose of this special issue is to gather contributions highlighting ongoing research related to remote sensing of sea surface salinity from spaceborne or airborne sensors, as well as combined use of satellite SSS with other observations (e.g. altimeter, SST, ...). In situ or laboratory measurements in support of improving forward models and retrieval algorithms are also welcome. Applied and theoretical research contributions concerning the multiple aspects of remote sensing of sea surface salinity will be considered.

The topics of interest include, but are not limited to:

  • Improvements in empirical or theoretical radiative transfer models
  • Mitigation techniques of external interference such as RFI, Sun, and land contamination
  • Comparison and validation of remote sensing products with in situ observations
  • Retrieval techniques for improved coastal SSS monitoring
  • High latitude SSS and oceans interactions with the cryosphere
  • Rain impact on SSS
  • Synergistic retrieval with other variables such as ice properties, sea surface temperature, or soil moisture
  • New instrument technology to enhance or expand SSS remote sensing capabilities

Dr. Emmanuel Philippe Dinnat
Dr. Xiaobin Yin
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

  • Sea Surface salinity
  • Ocean surface roughness
  • Microwave radiometry
  • Remote sensing
  • Spaceborne
  • Airborne
  • Forward model
  • Retrieval algorithm

Published Papers (16 papers)

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Editorial

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Open AccessEditorial
Editorial for the Special Issue “Sea Surface Salinity Remote Sensing”
Remote Sens. 2019, 11(11), 1300; https://doi.org/10.3390/rs11111300
Received: 27 May 2019 / Accepted: 30 May 2019 / Published: 31 May 2019
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Abstract
This Special Issue gathers papers reporting research on various aspects of remote sensing of sea surface salinity (SSS) and the use of satellites SSS in oceanography. It includes contributions presenting improvements in empirical or theoretical radiative transfer models; mitigation techniques of external interference [...] Read more.
This Special Issue gathers papers reporting research on various aspects of remote sensing of sea surface salinity (SSS) and the use of satellites SSS in oceanography. It includes contributions presenting improvements in empirical or theoretical radiative transfer models; mitigation techniques of external interference such as radio frequency interferences (RFI) and land contamination; comparisons and validation of remote sensing products with in situ observations; retrieval techniques for improved coastal SSS monitoring, high latitude SSS monitoring and assessment of ocean interactions with the cryosphere; and data fusion techniques combining SSS with sea surface temperature (SST). New instrument technology for the future of SSS remote sensing is also presented. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)

Research

Jump to: Editorial, Other

Open AccessArticle
Seasonal Variability of Retroflection Structures and Transports in the Atlantic Ocean as Inferred from Satellite-Derived Salinity Maps
Remote Sens. 2019, 11(7), 802; https://doi.org/10.3390/rs11070802
Received: 29 December 2018 / Revised: 27 March 2019 / Accepted: 28 March 2019 / Published: 3 April 2019
Cited by 1 | PDF Full-text (4412 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Three of the world’s most energetic regions are in the tropical and South Atlantic: the North Brazil Current Retroflection, the Brazil-Malvinas Confluence, and the Agulhas Current Retroflection. All three regions display offshore diversions of major boundary currents, which define the intensity of the [...] Read more.
Three of the world’s most energetic regions are in the tropical and South Atlantic: the North Brazil Current Retroflection, the Brazil-Malvinas Confluence, and the Agulhas Current Retroflection. All three regions display offshore diversions of major boundary currents, which define the intensity of the returning limb of the Atlantic meridional overturning circulation. In this work, we use a sea-surface salinity (SSS) satellite product, combined with a high-resolution numerical model and in situ measurements, in order to explore the seasonal variation of the surface currents and transports in these three regions. The analysis of the model output shows that the SSS patterns reflect the surface velocity structure, with the largest horizontal SSS gradients coinciding with those areas of highest velocity and the most predominant velocity vector being 90° anticlockwise (clockwise) from the horizontal SSS gradient in the northern (southern) hemisphere. This information is then applied to the SSS satellite product to obtain maps of water velocity and salt transports, leading to a quantitative tool to estimate both water and salt transports in key regions of the world ocean. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters
Remote Sens. 2019, 11(7), 750; https://doi.org/10.3390/rs11070750
Received: 14 February 2019 / Revised: 22 March 2019 / Accepted: 24 March 2019 / Published: 28 March 2019
Cited by 1 | PDF Full-text (6547 KB) | HTML Full-text | XML Full-text
Abstract
Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission [...] Read more.
Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
An Observational Perspective of Sea Surface Salinity in the Southwestern Indian Ocean and Its Role in the South Asia Summer Monsoon
Remote Sens. 2018, 10(12), 1930; https://doi.org/10.3390/rs10121930
Received: 31 October 2018 / Revised: 17 November 2018 / Accepted: 27 November 2018 / Published: 1 December 2018
Cited by 1 | PDF Full-text (11864 KB) | HTML Full-text | XML Full-text
Abstract
The seasonal variability of sea surface salinity anomalies (SSSAs) in the Indian Ocean is investigated for its role in the South Asian Summer Monsoon. We have observed an elongated spatial-feature of the positive SSSAs in the southwestern Indian Ocean before the onset of [...] Read more.
The seasonal variability of sea surface salinity anomalies (SSSAs) in the Indian Ocean is investigated for its role in the South Asian Summer Monsoon. We have observed an elongated spatial-feature of the positive SSSAs in the southwestern Indian Ocean before the onset of the South Asian Summer Monsoon (SASM) by using both the Aquarius satellite and the Argo float datasets. The maximum variable areas of SSSAs in the Indian Ocean are along (60 ° E–80 ° E) and symmetrical to the equator, divided into the southern and northern parts. Further, we have found that the annual variability of SSSAs changes earlier than that of sea surface temperature anomalies (SSTAs) in the corresponding areas, due to the change of wind stress and freshwater flux. The change of barrier layer thickness (BLT) anomalies is in phase with that of SSSAs in the southwestern Indian Ocean, which helps to sustain the warming water by prohibiting upwelling. Due to the time delay of SSSAs change between the northern and southern parts, SSSAs, therefore, take part in the seasonal process of the SASM via promoting the SSTAs gradient for the cross-equator currents. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions
Remote Sens. 2018, 10(11), 1772; https://doi.org/10.3390/rs10111772
Received: 14 September 2018 / Revised: 1 November 2018 / Accepted: 3 November 2018 / Published: 8 November 2018
Cited by 3 | PDF Full-text (6413 KB) | HTML Full-text | XML Full-text | Correction
Abstract
This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans (50N–90N). [...] Read more.
This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 N– 90 N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
SMAP and CalCOFI Observe Freshening during the 2014–2016 Northeast Pacific Warm Anomaly
Remote Sens. 2018, 10(11), 1716; https://doi.org/10.3390/rs10111716
Received: 5 October 2018 / Revised: 23 October 2018 / Accepted: 26 October 2018 / Published: 31 October 2018
Cited by 1 | PDF Full-text (4075 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Data from NASA’s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015–2016 in the Southern California Current System. Overall, the freshening was found to be related to [...] Read more.
Data from NASA’s Soil Moisture Active Passive Mission (SMAP) and from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) were used to examine the freshening that occurred during 2015–2016 in the Southern California Current System. Overall, the freshening was found to be related to the 2014–2016 Northeast Pacific Warm Anomaly. The primary goal was to determine the feasibility of using SMAP data to observe the surface salinity signal associated with the warming and its coastal impact. As a first step, direct comparisons were done with salinity from the CalCOFI data at one-meter depth. During 2015, SMAP was saltier than CalCOFI by 0.5 Practical Salinity Units (PSU), but biases were reduced to <0.1 PSU during 2016. South of 33°N, and nearer to the coast where upwelling dominates, SMAP was fresher in 2015 by almost 0.2 PSU. CalCOFI showed freshening of 0.1 PSU. North of 33°N, SMAP and CalCOFI saw significant freshening in 2016, SMAP by 0.4 PSU and CalCOFI by 0.2 PSU. Differences between SMAP and CalCOFI are consistent with the increased stratification in 2015 and changes in the mixed layer depth. SMAP observed freshening that reached the Baja California Coast. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Intercomparison of In-Situ and Remote Sensing Salinity Products in the Gulf of Mexico, a River-Influenced System
Remote Sens. 2018, 10(10), 1590; https://doi.org/10.3390/rs10101590
Received: 7 August 2018 / Revised: 29 September 2018 / Accepted: 1 October 2018 / Published: 4 October 2018
Cited by 3 | PDF Full-text (18629 KB) | HTML Full-text | XML Full-text
Abstract
The recent emergence of satellite-based sea surface salinity (SSS) measurements provides new opportunities for oceanographic research on freshwater influence in coastal environments. Several products currently exist from multiple observing platforms and processing centers, making product selection for different uses challenging. Here we evaluate [...] Read more.
The recent emergence of satellite-based sea surface salinity (SSS) measurements provides new opportunities for oceanographic research on freshwater influence in coastal environments. Several products currently exist from multiple observing platforms and processing centers, making product selection for different uses challenging. Here we evaluate four popular SSS datasets in the Gulf of Mexico (GoM) to characterize the error in each product versus in-situ observations: Two products from NASA’s Soil Moisture Active Passive (SMAP) mission, processed by Remote Sensing Systems (REMSS) (40 km and 70 km); one SMAP 60 km product from the Jet Propulsion Laboratory (JPL); and one 60 km product from ESA’s Soil Moisture Ocean Salinity (SMOS) mission. Overall, the four products are remarkably consistent on seasonal time scales, reproducing dominant salinity features. Towards the coast, 3 of the 4 products (JPL SMAP, REMSS 40 km SMAP, and SMOS) show increasing salty biases (reaching 0.7–1 pss) and Root Mean Square Error (RMSD) (reaching 1.5–2.5 pss), and a decreasing signal to noise ratio from 3 to 1. REMSS 40 km generally shows a lower RMSD than other products (~0.5 vs. ~1.1 pss) in the nearshore region. However, at some buoy locations, SMOS shows the lowest RMSD values, but has a higher bias overall (>0.2 vs. <0.1 pss). The REMSS 70km product is not consistent in terms of data availability in the nearshore region and performs poorly within 100 km of the coast, relative to other products. Additional analysis of the temporal structure of the errors over a range of scales (8/9-day to seasonal) shows significantly decreasing RMSD with increasing timescales across products. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Status of Aquarius and Salinity Continuity
Remote Sens. 2018, 10(10), 1585; https://doi.org/10.3390/rs10101585
Received: 5 September 2018 / Revised: 17 September 2018 / Accepted: 18 September 2018 / Published: 2 October 2018
Cited by 6 | PDF Full-text (6231 KB) | HTML Full-text | XML Full-text
Abstract
Aquarius is an L-band radar/radiometer instrument combination that has been designed to measure ocean salinity. It was launched on 10 June 2011 as part of the Aquarius/SAC-D observatory. The observatory is a partnership between the United States National Aeronautics and Space Agency (NASA), [...] Read more.
Aquarius is an L-band radar/radiometer instrument combination that has been designed to measure ocean salinity. It was launched on 10 June 2011 as part of the Aquarius/SAC-D observatory. The observatory is a partnership between the United States National Aeronautics and Space Agency (NASA), which provided Aquarius, and the Argentinian space agency, Comisiόn Nacional de Actividades Espaciales (CONAE), which provided the spacecraft bus, Satelite de Aplicaciones Cientificas (SAC-D). The observatory was lost four years later on 7 June 2015 when a failure in the power distribution network resulted in the loss of control of the spacecraft. The Aquarius Mission formally ended on 31 December 2017. The last major milestone was the release of the final version of the salinity retrieval (Version 5). Version 5 meets the mission requirements for accuracy, and reflects the continuing progress and understanding developed by the science team over the lifetime of the mission. Further progress is possible, and several issues remained unresolved at the end of the mission that are relevant to future salinity retrievals. The understanding developed with Aquarius is being transferred to radiometer observations over the ocean from NASA’s Soil Moisture Active Passive (SMAP) satellite, and salinity from SMAP with accuracy approaching that of Aquarius are already being produced. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Assessment of Aquarius Sea Surface Salinity
Remote Sens. 2018, 10(9), 1341; https://doi.org/10.3390/rs10091341
Received: 28 June 2018 / Revised: 17 August 2018 / Accepted: 18 August 2018 / Published: 22 August 2018
Cited by 9 | PDF Full-text (4992 KB) | HTML Full-text | XML Full-text
Abstract
Aquarius was the first NASA satellite to observe the sea surface salinity (SSS) over the global ocean. The mission successfully collected data from 25 August 2011 to 7 June 2015. The Aquarius project released its final version (Version-5) of the SSS data product [...] Read more.
Aquarius was the first NASA satellite to observe the sea surface salinity (SSS) over the global ocean. The mission successfully collected data from 25 August 2011 to 7 June 2015. The Aquarius project released its final version (Version-5) of the SSS data product in December 2017. The purpose of this paper is to summarize the validation results from the Aquarius Validation Data System (AVDS) and other statistical methods, and to provide a general view of the Aquarius SSS quality to the users. The results demonstrate that Aquarius has met the mission target measurement accuracy requirement of 0.2 psu on monthly averages on 150 km scale. From the triple point analysis using Aquarius, in situ field and Hybrid Coordinate Ocean Model (HYCOM) products, the root mean square errors of Aquarius Level-2 and Level-3 data are estimated to be 0.17 psu and 0.13 psu, respectively. It is important that caution should be exercised when using Aquarius salinity data in areas with high radio frequency interference (RFI) and heavy rainfall, close to the coast lines where leakage of land signals may significantly affect the quality of the SSS data, and at high-latitude oceans where the L-band radiometer has poor sensitivity to SSS. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics
Remote Sens. 2018, 10(8), 1232; https://doi.org/10.3390/rs10081232
Received: 22 June 2018 / Revised: 1 August 2018 / Accepted: 3 August 2018 / Published: 6 August 2018
Cited by 4 | PDF Full-text (4440 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the [...] Read more.
Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases
Remote Sens. 2018, 10(7), 1121; https://doi.org/10.3390/rs10071121
Received: 14 June 2018 / Revised: 29 June 2018 / Accepted: 30 June 2018 / Published: 15 July 2018
Cited by 19 | PDF Full-text (6716 KB) | HTML Full-text | XML Full-text
Abstract
The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric [...] Read more.
The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within ±0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes
Remote Sens. 2018, 10(6), 869; https://doi.org/10.3390/rs10060869
Received: 5 May 2018 / Revised: 24 May 2018 / Accepted: 28 May 2018 / Published: 4 June 2018
Cited by 4 | PDF Full-text (23484 KB) | HTML Full-text | XML Full-text
Abstract
Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS [...] Read more.
Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability to address the critical question of how climate variability and change affect the Arctic Ocean freshwater. The L-band microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) mission has been providing SSS measurements since April 2015, at approximately 60 km resolution with Arctic Ocean coverage in 1–2 days. With improved land/ice correction, the SMAP SSS algorithm that was developed at the Jet Propulsion Laboratory (JPL) is able to retrieve SSS in ice-free regions 35 km of the coast. SMAP observes a large-scale contrast in salinity between the Atlantic and Pacific sides of the Arctic Ocean, while retrievals within the Arctic Circle vary over time, depending on the sea ice coverage and river runoff. We assess the accuracy of SMAP SSS through comparative analysis with in situ salinity data collected by Argo floats, ships, gliders, and in field campaigns. Results derived from nearly 20,000 pairs of SMAP and in situ data North of 50°N collocated within a 12.5-km radius and daily time window indicate a Root Mean Square Difference (RMSD) less than ~1 psu with a correlation coefficient of 0.82 and a near unity regression slope over the entire range of salinity. In contrast, the Hybrid Coordinate Ocean Model (HYCOM) has a smaller RMSD with Argo. However, there are clear systematic biases in the HYCOM for salinity in the range of 25–30 psu, leading to a regression slope of about 0.5. In the region North of 65°N, the number of collocated samples drops more than 70%, resulting in an RMSD of about 1.2 psu. SMAP SSS in the Kara Sea shows a consistent response to discharge anomalies from the Ob’ and Yenisei rivers between 2015 and 2016, providing an assessment of runoff impact in a region where no in situ salinity data are available for validation. The Kara Sea SSS anomaly observed by SMAP is missing in the HYCOM SSS, which assimilates climatological runoffs without interannual changes. We explored the feasibility of using SMAP SSS to monitor the sea surface salinity variability at the major Arctic Ocean gateways. Results show that although the SMAP SSS is limited to about 1 psu accuracy, many large salinity changes are observable. This may lead to the potential application of satellite SSS in the Arctic monitoring system as a proxy of the upper ocean layer freshwater exchanges with subarctic oceans. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Comparison of the Retrieval of Sea Surface Salinity Using Different Instrument Configurations of MICAP
Remote Sens. 2018, 10(4), 550; https://doi.org/10.3390/rs10040550
Received: 25 January 2018 / Revised: 23 March 2018 / Accepted: 29 March 2018 / Published: 4 April 2018
Cited by 1 | PDF Full-text (27054 KB) | HTML Full-text | XML Full-text
Abstract
The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on [...] Read more.
The Microwave Imager Combined Active/Passive (MICAP) has been designed to simultaneously retrieve sea surface salinity (SSS), sea surface temperature (SST) and wind speed (WS), and its performance has also been preliminarily analyzed. To determine the influence of the first guess values uncertainties on the retrieved parameters of MICAP, the retrieval accuracies of SSS, SST, and WS are estimated at various noise levels. The results suggest that the errors on the retrieved SSS have not increased dues poorly known initial values of SST and WS, since the MICAP can simultaneously acquire SST information and correct ocean surface roughness. The main objective of this paper is to obtain the simplified instrument configuration of MICAP without loss of the SSS, SST, and WS retrieval accuracies. Comparisons are conducted between three different instrument configurations in retrieval mode, based on the simulation measurements of MICAP. The retrieval results tend to prove that, without the 23.8 GHz channel, the errors on the retrieved SSS, SST, and WS for MICAP could also satisfy the accuracy requirements well globally during only one satellite pass. By contrast, without the 1.26 GHz scatterometer, there are relatively large increases in the SSS, SST, and WS errors at middle/low latitudes. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessArticle
Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis
Remote Sens. 2018, 10(3), 485; https://doi.org/10.3390/rs10030485
Received: 23 January 2018 / Revised: 5 March 2018 / Accepted: 17 March 2018 / Published: 20 March 2018
Cited by 11 | PDF Full-text (4908 KB) | HTML Full-text | XML Full-text
Abstract
A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean [...] Read more.
A new methodology using a combination of debiased non-Bayesian retrieval, DINEOF (Data Interpolating Empirical Orthogonal Functions) and multifractal fusion has been used to obtain Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) fields over the North Atlantic Ocean and the Mediterranean Sea. The debiased non-Bayesian retrieval mitigates the systematic errors produced by the contamination of the land over the sea. In addition, this retrieval improves the coverage by means of multiyear statistical filtering criteria. This methodology allows obtaining SMOS SSS fields in the Mediterranean Sea. However, the resulting SSS suffers from a seasonal (and other time-dependent) bias. This time-dependent bias has been characterized by means of specific Empirical Orthogonal Functions (EOFs). Finally, high resolution Sea Surface Temperature (OSTIA SST) maps have been used for improving the spatial and temporal resolution of the SMOS SSS maps. The presented methodology practically reduces the error of the SMOS SSS in the Mediterranean Sea by half. As a result, the SSS dynamics described by the new SMOS maps in the Algerian Basin and the Balearic Front agrees with the one described by in situ SSS, and the mesoscale structures described by SMOS in the Alboran Sea and in the Gulf of Lion coincide with the ones described by the high resolution remotely-sensed SST images (AVHRR). Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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Open AccessCorrection
Correction: Olmedo, E., et al. Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions. Remote Sensing 2018, 10, 1772
Remote Sens. 2019, 11(8), 940; https://doi.org/10.3390/rs11080940
Received: 10 April 2019 / Accepted: 15 April 2019 / Published: 18 April 2019
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Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
Open AccessLetter
End-to-End Simulation of WCOM IMI Sea Surface Salinity Retrieval
Remote Sens. 2019, 11(3), 217; https://doi.org/10.3390/rs11030217
Received: 29 November 2018 / Revised: 10 January 2019 / Accepted: 17 January 2019 / Published: 22 January 2019
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Abstract
The Water Cycle Observation Mission (WCOM) is an Earth science mission focused on the observation of the water cycle global climate change intensity through three different payloads. WCOM’s main payload is an interferometric microwave imager (IMI). IMI is a tri-frequency, one-dimensional aperture synthesis [...] Read more.
The Water Cycle Observation Mission (WCOM) is an Earth science mission focused on the observation of the water cycle global climate change intensity through three different payloads. WCOM’s main payload is an interferometric microwave imager (IMI). IMI is a tri-frequency, one-dimensional aperture synthesis microwave radiometer operating at the L-, S-, and C-bands to perform measurements of soil moisture and ocean salinity. Focusing on sea surface salinity (SSS), an end-to-end simulator of WCOM/IMI has been realized and tested on climatological data. Results indicate a general agreement between original and retrieved SSS, with a single measurement root mean square error of 0.26 psu and with an orbital measurement of 0.17 psu in open sea. In accordance with previous studies, good results are obtained in open sea, while strong contamination is observed in coastal areas. Full article
(This article belongs to the Special Issue Sea Surface Salinity Remote Sensing)
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