https://doi.org/10.4081/aiol.2022.10862

Remote sensing of sea surface salinity: A bibliometric analysis

Vol. 13 No. 2 (2022): 50 Years of AIOL: Special Anniversary Issue
Submitted: 9 September 2022
Accepted: 3 November 2022
Published: 27 December 2022
sea surface salinity, remote sensing of the ocean, bibliometric network analysis
Abstract Views: 1164
PDF: 320
HTML: 54
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Flavia Zanon
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Napoli, Italy.
Cinzia Cesarano
Dipartimento di Scienze della Vita e dell’Ambiente, Università Politecnica delle Marche, Ancona, Italy.
Yuri Cotroneo
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Napoli, Italy.
Giannetta Fusco
https://orcid.org/0000-0003-1769-2456
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Napoli, Italy.
Giorgio Budillon
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Napoli, Italy.
Giuseppe Aulicino
giuseppe.aulicino@uniparthenope.it
https://orcid.org/0000-0001-6406-8715
Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli Parthenope, Napoli, Italy.

In recent years, rapid advances in technologies have allowed significant positive changes within the field of satellite observations of the global ocean. This paper reviews the available global scientific literature that focuses on the study of salinity by remote sensing, tracking its evolution and trends by combining social network analysis and bibliometrics. Furthermore, the study shows the relationships and co-occurrences between authors, countries and keywords retrieved from the abstracts and citations database provided by Scopus. An analysis of 581 publications has been carried out. The achieved results, which highlight a worldwide increase in scientific interest in this field over the last decade, may constitute a useful tool for a global vision and for a potential improvement in the international efforts employed in the study of salinity from remote sensing.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Crossref
Scopus
Google Scholar
Europe PMC
Appolloni L, Buonocore E, Russo GF, Franzese PP. (2020). The use of remote sensing for monitoring Posidonia oceanica and Marine Protected Areas: A systemic review. Ecological Questions 31:7-17. DOI: https://doi.org/10.12775/EQ.2020.009
Aulicino G, Cotroneo Y, Olmedo E, Cesarano C, Fusco G, Budillon G. (2019). In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products. Remote Sensing, 11:1361. DOI: https://doi.org/10.3390/rs11111361
Aulicino G, Cotroneo Y, De Ruggiero P, Buono A, Corcione V, Nunziata F et al. (2022). Remote Sensing Applications in Satellite Oceanography. Springer Series in Measurement Science and Technology. Springer Cham, Midtown Manhattan, USA. p. 181-209. DOI: https://doi.org/10.1007/978-3-030-82024-4_8
Aulicino G, Cotroneo Y, Ansorge I, Van Den Berg M, Cesarano C, Rivas MB, et al. (2018). Sea surface salinity and temperature in the southern Atlantic Ocean from South African icebreakers, 2010-2017. Earth System Science Data. 10:1227- 1236. DOI: https://doi.org/10.5194/essd-10-1227-2018
Bao S, Wang H, Zhang R, Yan H, Chen J. (2019). Comparison of Satellite-Derived Sea Surface Salinity Products from SMOS, Aquarius, and SMAP. Journal of Geophysical Research: Oceans. 124:1932-1944. DOI: https://doi.org/10.1029/2019JC014937
Bao S, Zhang R, Wang H, Yan H, Chen J, Wang Y. (2021). Correction of Satellite Sea Surface Salinity Products Using En-semble Learning Method. IEEE Access. 99:1-1. DOI: https://doi.org/10.1109/ACCESS.2021.3057886
Boutin J, Chao Y, Asher WE, Delcroix T, Drucker R, Drushka K, et al. (2016). Satellite and in Situ Salinity: Understanding Near-Surface Stratification and Subfootprint Variability. Bulletin of the American Meteorological Society. 97:1391-1407. DOI: https://doi.org/10.1175/BAMS-D-15-00032.1
Boutin J, Reul N, Koehler J, Martin A, Catany R, Guimbard S, et al. (2021). Satellite-based sea surface salinity designed for ocean and climate studies. Journal of Geophysical Research: Oceans. 126:e2021JC017676. DOI: https://doi.org/10.1029/2021JC017676
Boutin J, Reul N, Kohler J, Martin ACH, Catany R, Guimbard S, et al. (2021). Satellite-based Time-Series of Sea Surface Salinity designed for Ocean and Climate Studies. Journal of Geo-physical Research Ocean. 126. DOI: https://doi.org/10.1002/essoar.10507337.1
Buongiorno Nardelli B. (2020). A Deep Learning Network to Retrieve Ocean Hydrographic Profiles from Combined Satellite and In Situ Measurements. Remote Sensing. 12:3151. DOI: https://doi.org/10.3390/rs12193151
Champagne C, Rowlandson T, Berg A, et al. (2016). Satellite surface soil moisture from SMOS and Aquarius: Assessment for applications in agricultural landscapes. International Journal of Applied Earth Observation and Geoinformation. 143-154. DOI: https://doi.org/10.1016/j.jag.2015.09.004
Dinnat EP, Le Vine DM, Boutin J, Meissner T, Lagerloef G. (2019). Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters. Remote sensing. 11: 750. DOI: https://doi.org/10.3390/rs11070750
Duan P, Wang Y, Yin P. Remote Sensing Applications in Monitoring of Protected Areas: A Bibliometric Analysis. Remote sensing. 12:772. DOI: https://doi.org/10.3390/rs12050772
Cesarano C, Aulicino G, Cerrano C, Ponti M, Puce S. (2021). Scientific knowledge on marine beach litter: A bibliometric analysis. Marine Pollution Bulletin. 173. DOI: https://doi.org/10.1016/j.marpolbul.2021.113102
Cui X, Guo X, Wang Y, Wang X, Zhu W, Shie J, et al. Application of remote sensing to water environmental processes under a changing climate. Journal of Hydrology. 574:892–902. DOI: https://doi.org/10.1016/j.jhydrol.2019.04.078
Falco P, Castagno P, Cotroneo Y, Aulicino G, Budillon G, De Ruggiero P, et al. (2022). Measurements for Oceanography. Springer Series in Measurement Science and Technology. Springer Cham, Midtown Manhattan, USA. p. 51-81. DOI: https://doi.org/10.1007/978-3-030-82024-4_3
Fournier S, Lee T, Tang W, Steele M, Olmedo E. (2019). Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean. Remote Sensing. 11:3043. DOI: https://doi.org/10.3390/rs11243043
Geng Y, Chen W, Liu Z, Chui ASF, Han W, Liu Z, et al. (2017). A bibliometric review: Energy consumption and greenhouse gas emission in the residential sector. Journal of Cleaner Production. 159:301–316. DOI: https://doi.org/10.1016/j.jclepro.2017.05.091
Guimbard S, Reul N, Sabia R, Herlédan S, Khoury Hanna ZE, Piollé JF, et al. (2021). The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data. Remote Sensing. 13:4600. DOI: https://doi.org/10.3390/rs13224600
Hu K, Qi K, Guan Q, Wu C, Yu J, Qing Y, et al. (2017). A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016. Remote Sensing. 9:802. DOI: https://doi.org/10.3390/rs9080802
Jang E, Kim YJ, Im J, Park YG. (2021). Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches. GIScience & Remote Sensing. 58:138-160. DOI: https://doi.org/10.1080/15481603.2021.1872228
Kerr Y, Waldteufel P, Wigneron JP, Delwart S, Cabot F, Boutin J, et al. (2010). The SMOS mission: A new tool for monitoring key elements of the global water cycle. Proceedings of the IEEE. 98:666–687. DOI: https://doi.org/10.1109/JPROC.2010.2043032
Klemas V. (2011). Remote Sensing of Sea Surface Salinity: An Overview with Case Studies. Journal of Coastal Research. 27:830–838. DOI: https://doi.org/10.2112/JCOASTRES-D-11-00060.1
Khudzari JM, Kurian J, Tartakovsky B, Vijaya Raghavan GS. (2018). Bibliometric analysis of global research trends on microbial fuel cells using Scopus database. Biochemical Engineering Journal. 51-60. DOI: https://doi.org/10.1016/j.bej.2018.05.002
Le Vine DM, Kao M, Garvine RW, Sanders T. (1998). Remote Sensing of Ocean Salinity: Results from the Delaware Coastal Current Experiment. Journal of Atmospheric and Oceanic Technology. 15:1478-1484. DOI: https://doi.org/10.1175/1520-0426(1998)015<1478:RSOOSR>2.0.CO;2
Le Vine D, Lagerloef G, Torrusio S. (2010). Aquarius and remote sensing of sea surface salinity from space. Proceedings of the IEEE. 98:688–703. DOI: https://doi.org/10.1109/JPROC.2010.2040550
Li Y, Yin X, Zhou W, Lin M, Liu H, Li Y. (2022). Performance Simulation of the Payload IMR and MICAP Onboard the Chinese Ocean Salinity Satellite. IEEE Transactions on Geoscience and Remote Sensing. 60:1-16. DOI: https://doi.org/10.1109/TGRS.2021.3111026
Njoku EG, Rahmat-Samii Y, Sercel J, Wilson WJ, Moghaddam M. (1999). Evaluation of an inflatable antenna concept for microwave sensing of soil moisture and ocean salinity. IEEE Transactions on Geoscience and Remote Sensing. 371:63-78. DOI: https://doi.org/10.1109/36.739121
Olmedo E, Gabarró C, González-Gambau V, Martínez J, Ballabrera-Poy J, Turiel A, et al. (2018). Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions. Remote Sensing. 10:1772. DOI: https://doi.org/10.3390/rs10111772
Pablos M, Vall-Llossera M. (2014). SMOS and Aquarius Radiometers: Inter-Comparison Over Selected Targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7:9. DOI: https://doi.org/10.1109/JSTARS.2014.2321455
Perumal K, Muthuramalingam S. (2021). Global sources, abundance, size, and distribution of microplastics in marine sediments - A critical review. Estuarine, Coastal and Shelf Science. 264. DOI: https://doi.org/10.1016/j.ecss.2021.107702
Reul N, Grodsky SA, Arias M, et al. (2020). Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010-2019). Remote Sensing of Environment. 242. DOI: https://doi.org/10.1016/j.rse.2020.111769
Reuters T. (2008). Whitepaper using bibliometrics: a guide to evaluating research performance with citation data. Available at: http://openscience.ens.fr/MARIE_FARGE/CONFERENCES/2014_12_02_BIBLIOMETRIE_ET_EVALUATION_DE_LA_RECHERCHE_ABDU_PARIS/InCites_Thomson-Reuters.pdf
Thomann GC. (1976). Experimental Results of the Remote Sensing of Sea-Surface Salinity at 21-cm Wavelength. IEEE Transactions on Geoscience Electronics. 14:198-214. DOI: https://doi.org/10.1109/TGE.1976.294450
Thouvenin-Masson C, Boutin J, Vergely JL, et al. (2022). Satellite and in situ sampling mismatches: consequences for the estimate of satellite sea surface salinity uncertainties. Remote Sensing. 14. DOI: https://doi.org/10.20944/preprints202202.0260.v1
Ulaby FT, Moore RK, Fung K. (1981). Fundamentals and radiometry. Microwave Remote Sensing Active and Passive. Artech House Publishers, London, UK. 456 pp.
Van Eck N, Waltman L, Van den Berg J, Kaymak U. (2006). Visualizing the computational intelligence field. IEEE Computational Intelligence Magazine. 1:6–10. DOI: https://doi.org/10.1109/CI-M.2006.248043
Van Eck NJ, Waltman L. (2021). VOSviewer Manual 1.6.17. DOI: https://doi.org/10.1109/MCI.2006.329702
Vinogradova N, Lee T, Boutin J, Drushka K, Fournier S, Sabia R, et al. (2019). Satellite Salinity Observing System: Recent Discoveries and the Way Forward. Frontiers in Marine Science. 6:243. DOI: https://doi.org/10.3389/fmars.2019.00243

Supporting Agencies

This work was partly funded by the Italian Ministry of University and Research (MUR) under the Antarctica National Research Programme (PNRA) entitled ”SWIMMING - Sea ice-wave interaction monitoring for marginal ice navigation”, PNRA18_00298.

How to Cite

Zanon, F., Cesarano, C., Cotroneo, Y., Fusco, G., Budillon, G., & Aulicino, G. (2022). Remote sensing of sea surface salinity: A bibliometric analysis. Advances in Oceanography and Limnology, 13(2). https://doi.org/10.4081/aiol.2022.10862
Copyright (c) 2022 The Author(s) Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.