Acta Oceanologica Sinica

, Volume 34, Issue 7, pp 89–96 | Cite as

Impact of rain-induced sea surface roughness variations on salinity retrieval from the Aquarius/SAC-D satellite

  • Wentao Ma
  • Xiaofeng YangEmail author
  • Yang Yu
  • Guihong Liu
  • Ziwei Li
  • Cheng Jing


Rainfall has two significant effects on the sea surface, including salinity decreasing and surface becoming rougher, which have further influence on L-band sea surface emissivity. Investigations using the Aquarius and TRMM 3B42 matchup dataset indicate that the retrieved sea surface salinity (SSS) is underestimated by the present Aquarius algorithm compared to numerical model outputs, especially in cases of a high rain rate. For example, the bias between satellite-observed SSS and numerical model SSS is approximately 2 when the rain rate is 25 mm/h. The bias can be eliminated by accounting for rain-induced roughness, which is usually modeled by rain-generated ring-wave spectrum. The rain spectrum will be input into the Small Slope Approximation (SSA) model for the simulation of sea surface emissivity influenced by rain. The comparison with theoretical model indicated that the empirical model of rain spectrumis more suitable to be used in the simulation. Further, the coefficients of the rain spectrum are modified by fitting the simulations with the observations of the 2–year Aquarius and TRMM matchup dataset. The calculations confirm that the sea surface emissivity increases with the wind speed and rain rate. The increase induced by the rain rate is rapid in the case of low rain rate and low wind speed. Finally, a modified model of sea surface emissivity including the rain spectrum is proposed and validated by using the matchup dataset in May 2014. Compared with observations, the bias of the rain-induced sea surface emissivity simulated by the modified modelis approximately 1e–4, and the RMSE is slightly larger than 1e–3. With using more matchup data, thebias between model retrieved sea surface salinities and observationsmay be further corrected, and the RMSE may be reduced to less than 1 in the cases of low rain rate and low wind speed.


Aquarius salinity remote sensing rain L-band emissivity 


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  1. Bliven L F, Sobieski P W, Craeye C. 1997. Rain generated ring-waves: measurements and modelling for remote sensing. IntJRemote Sens, 18(1): 221–228Google Scholar
  2. Boutin J, Martin N, Reverdin G, et al. 2013. Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain. Ocean Sci, 9(1): 183–192CrossRefGoogle Scholar
  3. 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, 119(8): 5533–5545Google Scholar
  4. Boutin J, Martin N, Yin Xiaobin, et al. 2012. First assessment of SMOS data over open ocean: Part II-Sea surface salinity. IEEE Trans- Geosci Remote Sens, 50(5): 1662–1675CrossRefGoogle Scholar
  5. Chassignet E P, Hurlburt H E, Metzger E J, et al. 2009. US GODAE: global ocean prediction with the HYbrid coordinate ocean model (HYCOM). Oceanography, 22(2): 64–75CrossRefGoogle Scholar
  6. Contreras R F, Plant W J. 2006. Surface effect of rain on microwave backscatter from the ocean: Measurements and modeling. Journal of Geophysical Research: Oceans (1978–2012), 111(C8): C08019CrossRefGoogle Scholar
  7. Craeye C, Sobieski P W, Bliven L F. 1997. Scattering by artificial wind and rain roughened water surfaces at oblique incidences. IntJRemote Sens, 18(10): 2241–2246Google Scholar
  8. Durden S L, Vesecky J F. 1985. A physical radar cross-section model for a wind-driven sea with swell. IEEE JOceanic Eng, 10(4): 445–451CrossRefGoogle Scholar
  9. Felton C S, Subrahmanyam B, Murty V S N, et al. 2014. Estimation of the barrier layer thickness in the Indian Ocean using Aquarius Salinity. Journal of Geophysical Research: Oceans, 119(7): 4200–4213Google Scholar
  10. Font J, Camps A, Borges A, et al. 2010. SMOS: The challenging sea surface salinity measurement from space. Proc IEEE, 98(5): 649–665CrossRefGoogle Scholar
  11. Johnson J T, Zhang Min. 1999. Theoretical study of the small slope approximation for ocean polarimetric thermal emission. IEEE Trans Geosci Remote Sens, 37(5): 2305–2316CrossRefGoogle Scholar
  12. Kerr Y H, Waldteufel P, Wigneron J-P, et al. 2010. The SMOS mission: New tool for monitoring key elements ofthe global water cycle. Proc IEEE, 98(5): 666–687CrossRefGoogle Scholar
  13. Lagerloef G, Colomb F R, Le Vine D, et al. 2008. The Aquarius/SAC-D mission: Designed to meet the salinity remote-sensing challenge. Oceanography, 21(1): 68–81CrossRefGoogle Scholar
  14. Le Vine D M, Lagerloef G S E, Torrusio S E. 2010. Aquarius and remote sensing of sea surface salinity from space. Proc IEEE, 98(5): 688–703CrossRefGoogle Scholar
  15. Ma Wentao, Yang Xiaofeng, Liu Guihong, et al. 2014. An Improved Model for L-Band Brightness Temperature Estimation Over Foam-Covered Seas Under Low and Moderate Winds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3784–3793CrossRefGoogle Scholar
  16. Meissner T, Wentz F J. 2004. The complex dielectric constant of pure and sea water from microwave satellite observations. IEEE Trans Geosci Remote Sens, 42(9): 1836–1849CrossRefGoogle Scholar
  17. Qu Tangdong, Song Y T, Maes C. 2014. Sea surface salinity and barrier layer variability in the equatorial Pacific as seen from Aquarius and Argo. Journal of Geophysical Research: Oceans, 119(1): 15–29Google Scholar
  18. Reynolds R W, Smith T M, Liu Chunying, et al. 2007. Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22): 5473–5496CrossRefGoogle Scholar
  19. Sobieski P, Craeye C, Bliven L F. 2009. A relationship between rain radar reflectivity and height elevation variance of ringwaves due to the impact of rain on the sea surface. Radio Science, 44(3): CiteID RS3005Google Scholar
  20. Tang Wenqing, Yueh S, Fore A, et al. 2013. The rain effect on Aquarius' L-band sea surface brightness temperature and radar backscatter. Remote Sens Environ, 137: 147–157CrossRefGoogle Scholar
  21. Tang Wenqing, Yueh S H, Fore A G, et al. 2014. Uncertainty of Aquarius sea surface salinity retrieved under rainy conditions and its implication on the water cycle study. Journal of Geophysical Research: Oceans, 119(8): 4821–4839Google Scholar
  22. Terray L, Corre L, Cravatte S, et al. 2012. Near-surface salinity as nature's rain gauge to detect human influence on the tropical water cycle. Journal of Climate, 25(3): 958–977CrossRefGoogle Scholar
  23. Wentz F J. 2005. The effect of clouds and rain on Aquarius salinity retrieval. Remote Sensing System Technical Memorandum, 3031805Google Scholar
  24. Wentz F J, Le Vine David M. 2013. Aquarius Salinity Retrieval Algorithm. Algorithm Theoretical Basis DocumentGoogle Scholar
  25. Yin Xiaobin, Boutin J, Martin N, et al. 2012a. Optimization of L-band sea surface emissivity models deduced from SMOS data. IEEE Trans Geosci Remote Sens, 50(5): 1414–1426CrossRefGoogle Scholar
  26. Yin Xiaobin, Boutin J, Spurgeon P. 2012b. First assessment of SMOS data over open ocean: Part I—Pacific Ocean. IEEE Trans Geosci Remote Sens, 50(5): 1648–1661CrossRefGoogle Scholar
  27. Yueh S H, Dinardo S J, Fore A G, et al. 2010. Passive and active L-band microwave observations and modeling of ocean surface winds. IEEE Trans Geosci Remote Sens, 48(8): 3087–3100CrossRefGoogle Scholar
  28. Yueh S H, Tang Wenqing, Fore A G, et al. 2013. L-band passive and active microwave geophysical model functions of ocean surface winds and applications to Aquarius retrieval. IEEE Trans Geosci Remote Sens, 51(9): 4619–4632CrossRefGoogle Scholar

Copyright information

© The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Wentao Ma
    • 1
    • 2
  • Xiaofeng Yang
    • 2
    Email author
  • Yang Yu
    • 2
  • Guihong Liu
    • 2
  • Ziwei Li
    • 2
  • Cheng Jing
    • 2
  1. 1.College of Physical and Environmental OceanographyOcean University of ChinaQingdaoChina
  2. 2.State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina

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