CHEN Jian, YOU Xiaobao, XIAO Yiguo, ZHANG Ren, WANG Gongjie, BAO Senliang. A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields[J]. Acta Oceanologica Sinica, 2017, 36(7): 15-31. doi: 10.1007/s13131-017-1079-y
Citation: CHEN Jian, YOU Xiaobao, XIAO Yiguo, ZHANG Ren, WANG Gongjie, BAO Senliang. A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields[J]. Acta Oceanologica Sinica, 2017, 36(7): 15-31. doi: 10.1007/s13131-017-1079-y

A performance evaluation of remotely sensed sea surface salinity products in combination with other surface measurements in reconstructing three-dimensional salinity fields

doi: 10.1007/s13131-017-1079-y
  • Received Date: 2016-05-06
  • Rev Recd Date: 2017-01-17
  • Several remotely sensed sea surface salinity (SSS) retrievals with various resolutions from the soil moisture and ocean salinity (SMOS) and Aquarius/SAC-D missions are applied as inputs for retrieving salinity profiles (S) using multilinear regressions. The performance is evaluated using a total root mean square (RMS) error, different error sources, and the feature resolutions of the retrieved S fields. In the mixed layer of the salinity, the SSS-S regression coefficients are uniformly large. The SSS inputs yield smaller RMS errors in the retrieved S with respect to Argo profiles as their spatial or temporal resolution decreases. The projected SSS errors are dominant, and the retrieved S values are more accurate than those of climatology in the tropics except for the tropical Atlantic, where the regression errors are abnormally large. Below that level, because of the influence of a sea level anomaly, the areas of high-accuracy S values shift to higher latitudes except in the high-latitude southern oceans, where the projected SSS errors are abnormally large. A spectral analysis suggests that the CATDS-0.25° results are much noisier and that the BEC-L4-0.25° results are much smoother than those of the other retrievals. Aquarius-CAP-1° generates the smallest RMS errors, and Aquarius-V2-1° performs well in depicting large-scale phenomena. BEC-L3-0.25°, which has small RMS errors and remarkable mesoscale energy, is the best fit for portraying mesoscale features in the SSS and retrieved S fields. The current priority for retrieving S is to improve the reliability of satellite SSS especially at middle and high latitudes, by developing advanced algorithms, combining both sensors, or weighing between accuracy and resolutions.
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  • Agarwal N, Sharma R, Basu S, et al. 2007. Derivation of salinity profiles in the Indian Ocean from satellite surface observations. IEEE Geoscience and Remote Sensing Letters, 4(2): 322-325
    Ando K, McPhaden M J. 1997. Variability of surface layer hydrography in the tropical Pacific Ocean. Journal of Geophysical Research: Oceans (1978–2012), 102(C10): 23063-23078
    Asher W E, Jessup A T, Branch R, et al. 2014. Observations of rain-induced near-surface salinity anomalies. Journal of Geophysical Research: Oceans (1978–2012), 119(8): 5483-5500
    Ballabrera-Poy J, Mourre B, Garcia-Ladona E, et al. 2009. Linear and non-linear T-S models for the eastern North Atlantic from Argo data: role of surface salinity observations. Deep-Sea Research: Part I. Oceanographic Research Papers, 56(10): 1605-1614
    Boutin J, Martin N, Reverdin G, et al. 2014. Sea surface salinity under rain cells: SMOS satellite and in situ drifters observations. Journal of Geophysical Research: Oceans (1978–2012), 119(8): 5533-5545
    Buongiorno Nardelli B. 2012. A novel approach for the high-resolution interpolation of in situ sea surface salinity. Journal of Atmospheric and Oceanic Technology, 29(6): 867-879
    Buongiorno Nardelli B. 2013. Vortex waves and vertical motion in a mesoscale cyclonic eddy. Journal of Geophysical Research: Oceans (1978–2012), 118(10): 5609-5624
    Buongiorno Nardelli B, Guinehut S, Pascual A, et al. 2012. Towards high resolution mapping of 3-D mesoscale dynamics from observations. Ocean Science, 8(5): 885-901
    Buongiorno Nardelli B, Santoleri R. 2004. Reconstructing synthetic profiles from surface data. Journal of Atmospheric and Oceanic Technology, 21(4): 693-703
    Buongiorno Nardelli B, Santoleri R. 2005. Methods for the reconstruction of vertical profiles from surface data: multivariate analyses, residual GEM, and variable temporal signals in the North Pacific Ocean. Journal of Atmospheric and Oceanic Technology, 22(11): 1762-1781
    Carnes M R, Teague W J, Mitchell J L. 1994. Inference of subsurface thermohaline structure from fields measurable by satellite. Journal of Atmospheric and Oceanic Technology, 11(2): 551-566
    Delcroix T, Hénin C, Porte V, et al. 1996. Precipitation and sea-surface salinity in the tropical Pacific Ocean. Deep-Sea Research: Part I. Oceanographic Research Papers, 43(7): 1123-1141
    Dinnat E P, Boutin J, Yin Xiaobin, et al. 2014. Inter-comparison of SMOS and Aquarius sea surface salinity: effects of the dielectric constant and vicarious calibration. In: Proceedings of the 13th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment. Pasadena, CA: IEEE, 55–60
    Drucker R, Riser S C. 2014. Validation of Aquarius sea surface salinity with Argo: analysis of error due to depth of measurement and vertical salinity stratification. Journal of Geophysical Research: Oceans (1978–2012), 119(7): 4626-4637
    Font J, Boutin J, Reul N, et al. 2010. Overview of SMOS level 2 ocean salinity processing and first results. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Honolulu, HI: IEEE, 3146–3149
    Fox D N, Teague W J, Barron C N, et al. 2002. The modular ocean data assimilation system (MODAS). Journal of Atmospheric and Oceanic Technology, 19(2): 240-252
    Guimbard S, Gourrion J, Portabella M, et al. 2012. SMOS semi-empirical ocean forward model adjustment. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1676-1687
    Guinehut S, Dhomps A L, Larnicol G, et al. 2012. High Resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Science Discussions, 9(2): 1313-1347
    Guinehut S, Le Traon P Y, Larnicol G. 2006. What can we learn from Global Altimetry/Hydrography comparisons?. Geophysical Research Letters, 33(10): L10604
    Guinehut S, Le Traon P Y, Larnicol G, et al. 2004. Combining Argo and remote–sensing data to estimate the ocean three-dimensional temperature fields—A first approach based on simulated observations. Journal of Marine Systems, 46(1–4): 85-98
    Hernandez O, Boutin J, Kolodziejczyk N, et al. 2014. SMOS salinity in the subtropical North Atlantic salinity maximum: 1. Comparison with Aquarius and in situ salinity. Journal of Geophysical Research: Oceans (1978–2012), 119(12): 8878-8896
    Johnson J T, Zhang Min. 1999. Theoretical study of the small slope approximation for ocean polarimetric thermal emission. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2305-2316
    Jordà G, Gomis D. 2010. Accuracy of SMOS level 3 SSS products related to observational errors. IEEE Transactions on Geoscience and Remote Sensing, 48(4): 1694-1701
    Kerr Y H, Waldteufel P, Wigneron J P, et al. 2010. The SMOS mission: new tool for monitoring key elements of the global water cycle. Proceedings of the IEEE, 98(5): 666-687
    Larnicol G, Guinehut S, Rio M H, et al. 2006. The global observed ocean products of the French Mercator project. In: Proceedings of the Symposium on 15 Years of progress in radar altimetry. Noordwijk, Netherlands: ESA Special Publication
    Le Vine D M, Lagerloef G S E, Torrusio S E. 2010. Aquarius and remote sensing of sea surface salinity from space. Proceedings of the IEEE, 98(5): 688-703
    Maes C, Behringer D. 2000. Using satellite-derived sea level and temperature profiles for determining the salinity variability: a new approach. Journal of Geophysical Research: Oceans (1978–2012), 105(C4): 8537-8547
    Meinen C S, Watts D R. 2000. Vertical structure and transport on a transect across the North Atlantic Current near 42°N: time series and mean. Journal of Geophysical Research: Oceans (1978–2012), 105(C9): 21869-21891
    Meissner T, Wentz F J. 2012. The emissivity of the ocean surface between 6 and 90 GHz over a large range of wind speeds and earth incidence angles. IEEE Transactions on Geoscience and Remote Sensing, 50(8): 3004-3026
    Meissner T, Wentz F, Hilburn K, et al. 2012. The Aquarius salinity retrieval algorithm. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE
    Meissner, T, Wentz F, Ricciardulli L. 2014. The emission and scattering of L-band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor. Journal of Geophysical Research: Oceans (1978–2012), 119(9): 6499-6522
    Mitchell D A, Wimbush M, Watts D R, et al. 2004. The residual GEM technique and its application to the southwestern Japan/East Sea. Journal of Atmospheric and Oceanic Technology, 21(12): 1895-1909
    Mulet S, Rio M H, Mignot A, et al. 2012. A new estimate of the global 3D geostrophic ocean circulation based on satellite data and in-situ measurements. Deep-Sea Research: Part Ⅱ. Topical Studies in Oceanography, 77–80: 70-81
    Ohno Y, Kobayashi T, Iwasaka N, et al. 2004. The mixed layer depth in the North Pacific as detected by the Argo floats. Geophysical Research Letters, 31(11): L11306
    Pablos M, Piles M, González-Gambau V, et al. 2014. SMOS and Aquarius radiometers: inter-comparison over selected targets. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3833-3844
    Reagan J, Boyer T, Antonov J, et al. 2014. Comparison analysis between Aquarius sea surface salinity and World Ocean Database in situ analyzed sea surface salinity. Journal of Geophysical Research: Oceans (1978–2012), 119(11): 8122-8140
    Reynolds R W, Chelton D B. 2010. Comparisons of daily sea surface temperature analyses for 2007–08. Journal of Climate, 23(13): 3545-3562
    Telszewski M, Chazottes A, Schuster U, et al. 2009. Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network. Biogeosciences Discussions, 6(2): 3373-3414
    Turiel A, Nieves V, García-Ladona E, et al. 2009. The multifractal structure of satellite sea surface temperature maps can be used to obtain global maps of streamlines. Ocean Science, 5(4): 447-460
    Wu Xiangbai, Yan Xiaohai, Jo Y H, et al. 2012. Estimation of subsurface temperature anomaly in the North Atlantic using a self-organizing map neural network. Journal of Atmospheric and Oceanic Technology, 29(11): 1675-1688
    Xie P, Boyer T, Bayler E, et al. 2014. An in situ-satellite blended analysis of global sea surface salinity. Journal of Geophysical Research: Oceans (1978–2012), 119(9): 6140-6160
    Yueh S H, Chaubell J. 2012. Sea surface salinity and wind retrieval using combined passive and active L-band microwave observations. IEEE Transactions on Geoscience and Remote Sensing, 50(4): 1022-1032
    Yueh S, Tang Wenqing, Fore A, et al. 2014. Aquarius geophysical model function and combined active passive algorithm for ocean surface salinity and wind retrieval. Journal of Geophysical Research: Oceans (1978–2012), 119(8): 5360-5379
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