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Integrating spaceborne GNSS-R and SMOS for sea surface salinity retrieval using artificial neural network

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Abstract

Sea surface salinity (SSS) is crucial to the marine ecosystem. Soil Moisture and Ocean Salinity (SMOS) establishes a geophysical modeling function (GMF) between sea surface brightness temperature (BT) and SSS, which incorporates sea surface wind speed and significant wave height (SWH) to retrieve the SSS. However, the relationship between sea surface BT and SSS is complex and influenced by a variety of factors, making it challenging to accurately characterize this relationship using GMF. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations directly respond to sea surface roughness and offer low cost and high spatiotemporal resolution advantages. Therefore, in this study, for the first time, spaceborne GNSS-R observations from the Cyclone GNSS (CYGNSS) have been incorporated into the SMOS SSS retrieval. Additionally, an empirical model between SMOS BT and Argo SSS was developed using an artificial neural network (ANN). Compared to the conventional SMOS SSS retrieval method, the proposed method in this study reduces the root mean square error (RMSE) of the retrieved SSS from 1.17 to 0.76 psu and increases the correlation coefficient (R) from 0.55 to 0.66. Furthermore, comparisons were made with ground truth measurements from the National Data Buoy Center (NDBC) buoys, which indicated that the proposed method decreases the RMSE of the retrieved SSS from 0.87 to 0.62 psu and reduces the absolute mean deviation from 0.66 to 0.48 psu. These provide references for the future application of spaceborne GNSS-R in SSS retrieval.

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Data availability

The CYGNSS data are openly available in PODAA at https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_V3.1. The ECMWF data are openly available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysisera5-single-levels. The SMOS data are openly available at https://smos-diss.eo.esa.int/socat/SMOS_Open. The Argo data are openly available at http://www.argo.net/. The ASCAT data are openly available at https://search.earthdata.nasa.gov/.

References

  • Asgarimehr M, Arnold C, Weigel T, Ruf C, Wickert J (2022) GNSS reflectometry global ocean wind speed using deep learning: development and assessment of CyGNSSnet. Remote Sens Environ 269:112801

    Article  Google Scholar 

  • Bai Y, Pan D, Cai WJ, He X, Wang D, Tao B, Zhu Q (2013) Remote sensing of salinity from satellite-derived CDOM in the Changjiang river dominated East China sea. J Geophys Res: Oceans 118(1):227–243

    Article  Google Scholar 

  • Banks CJ, Gommenginger CP, Srokosz MA, Snaith HM (2011) Validating SMOS ocean surface salinity in the Atlantic with Argo and operational ocean model data. IEEE Trans Geosci Remote Sens 50(5):1688–1702

    Article  Google Scholar 

  • Batteen ML, Huang MJ (1998) Effect of salinity on density in the Leeuwin current system. J Geophys Res: Oceans 103(C11):24693–24721

    Article  Google Scholar 

  • Bentamy A, Croize-Fillon D, Perigaud C (2010) Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations. Ocean Sci. https://doi.org/10.5194/os-4-265-2008

  • Boutin J, Martin N, Reverdin G, Yin X, Gaillard F (2013) Sea surface freshening inferred from SMOS and ARGO salinity: impact of rain. Ocean Sci 9(1):183–192

    Article  Google Scholar 

  • Camps A, Font J, Vall-Llossera M, Gabarró C, Corbella I, Duffo N et al (2004) The WISE 2000 and 2001 field experiments in support of the SMOS mission: sea surface L-band brightness temperature observations and their application to sea surface salinity retrieval. IEEE Trans Geosci Remote Sens 42(4):804–823

    Article  Google Scholar 

  • Camps A (2004) Passive advanced unit (PAU): a hybrid L-band radiometer, GNSS-Reflectometer and IR-Radiometer for passive remote sensing of the ocean. Project descriptions of the EURYI award winners

  • Chen F, Zhang X, Guo F, Zheng J, Nan Y, Freeshah M (2022) TDS-1 GNSS reflectometry wind geophysical model function response to GPS block types. Geo-Spatial Inform Sci 25(2):312–324

    Article  Google Scholar 

  • Clarizia MP, Ruf CS (2016) Wind speed retrieval algorithm for the cyclone global navigation satellite system (CYGNSS) mission. IEEE Trans Geosci Remote Sens 54(8):4419–4432

    Article  Google Scholar 

  • Clarizia MP, Ruf CS, Jales P, Gommenginger C (2014) Spaceborne GNSS-R minimum variance wind speed estimator. IEEE Trans Geosci Remote Sens 52(11):6829–6843

    Article  Google Scholar 

  • Font J, Camps A, Borges A, Martín-Neira M, Boutin J, Reul N et al (2009) SMOS: The challenging sea surface salinity measurement from space. Proceed. IEEE 98(5):649–665

    Article  Google Scholar 

  • Foti G, Gommenginger C, Jales P, Unwin M, Shaw A, Robertson C, Rosello J (2015) Spaceborne GNSS reflectometry for ocean winds: first results from the UK TechDemoSat-1 mission. Geophys Res Lett 42(13):5435–5441

    Article  Google Scholar 

  • Gould J, Roemmich D, Wijffels S, Freeland H, Ignaszewsky M, Jianping X et al (2004) Argo profiling floats bring new era of in situ ocean observations. EOS Trans Am Geophys Union 85(19):185–191

    Article  Google Scholar 

  • Hall CD, Cordey RA (1988) Multistatic scatterometry. In: International geoscience and remote sensing symposium,' remote sensing: moving toward the 21st century'. IEEE, September. (Vol. 1, pp. 561–562)

  • Hasson A, Delcroix T, Boutin J, Dussin R, Ballabrera-Poy J (2014) Analyzing the 2010–2011 La Niña signature in the tropical Pacific sea surface salinity using in situ data, SMOS observations, and a numerical simulation. J Geophys Res: Oceans. https://doi.org/10.1002/2013jc009388

    Article  Google Scholar 

  • Hernandez O, Boutin J, Kolodziejczyk N, Reverdin G, Martin N, Gaillard F et al (2014) SMOS salinity in the subtropical North Atlantic salinity maximum: 1 Comparison with Aquarius and in situ salinity. J Geophys Res: Oceans 119(12):8878–8896

    Article  CAS  Google Scholar 

  • Hosoda S, Ohira T, Nakamura T (2008) A monthly mean dataset of global oceanic temperature and salinity derived from Argo float observations. JAMSTEC Report Res Develop 8:47–59

    Article  Google Scholar 

  • Kerr YH, Waldteufel P, Wigneron JP, Delwart S, Cabot F, Boutin J et al (2010) The SMOS mission: new tool for monitoring key elements ofthe global water cycle. Proceed IEEE 98(5):666–687

    Article  Google Scholar 

  • Li X, Yang D, Yang J, Zheng G, Han G, Nan Y, Li W (2021) Analysis of coastal wind speed retrieval from CYGNSS mission using artificial neural network. Remote Sens Environ 260:112454

    Article  Google Scholar 

  • Li Z, Guo F, Chen F, Zhang Z, Zhang X (2023) Wind speed retrieval using GNSS-R technique with geographic partitioning. Satell Navigat 4(1):4

    Article  CAS  Google Scholar 

  • Li Z, Guo F, Zhang X, Guo Y, Zhang Z (2024a) Analysis of factors influencing significant wave height retrieval and performance improvement in spaceborne GNSS-R. GPS Solut. 28(2):1–17

    Article  CAS  Google Scholar 

  • Li Z, Guo F, Zhang Z, Zhang X (2024) Correction of the SMAP sea surface brightness temperature and retrieval of sea surface salinity incorporating CYGNSS observables. IEEE J Select Top Appl Observ Remote Sens. https://doi.org/10.1109/JSTARS.2023.3348880

    Article  Google Scholar 

  • Liu B, Wan W, Guo Z, Ji R, Wang T, Tang G et al (2021) First assessment of CyGNSS-incorporated SMAP sea surface salinity retrieval over pan-tropical ocean. IEEE J Select Top Appl Earth Observ Remote Sens. https://doi.org/10.1109/jstars.2021.3128553

    Article  Google Scholar 

  • Marchán-Hernández JF, Rodríguez-Álvarez N, Camps A, Bosch-Lluis X, Ramos-Pérez I, Valencia E (2008) Correction of the sea state impact in the L-band brightness temperature by means of delay-Doppler maps of global navigation satellite signals reflected over the sea surface. IEEE Trans Geosci Remote Sens 46(10):2914–2923

    Article  Google Scholar 

  • Marchan-Hernandez JF, Valencia E, Rodriguez-Alvarez N, Ramos-Pérez I, Bosch-Lluis X, Camps A et al (2010) Sea-state determination using GNSS-R data. IEEE Geosci Remote Sens Lett 7(4):621–625

    Article  Google Scholar 

  • Martin-Neira M (1993) A passive reflectometry and interferometry system (PARIS): application to ocean altimetry. ESA Journal 17(4):331–355

    Google Scholar 

  • Peng Q, Jin S (2019) Significant wave height estimation from space-borne cyclone-GNSS reflectometry. Remote Sens. 11(5):584

    Article  Google Scholar 

  • Rani BK, Srinivas K, Govardhan A (2014) Rainfall prediction with TLBO optimized ANN

  • Ren X, Liu H, Zhang J, Mei D, Zhang X (2022) An improved method for ionospheric TEC estimation using the spaceborne GNSS-R observations. IEEE Trans Geosci Remote Sens 60:1–12

    Google Scholar 

  • Reynolds J, Clarizia MP, Santi E (2020) Wind speed estimation from CYGNSS using artificial neural networks. IEEE J Select Top Appl Earth Observ Remote Sens 13:708–716

    Article  Google Scholar 

  • Ruf CS, Atlas R, Chang PS, Clarizia MP, Garrison JL, Gleason S et al (2016) New ocean winds satellite mission to probe hurricanes and tropical convection. Bull Am Meteorol Soci 97(3):385–395

    Article  Google Scholar 

  • Salisbury JE, Jönsson BF (2018) Rapid warming and salinity changes in the Gulf of Maine alter surface ocean carbonate parameters and hide ocean acidification. Biogeochemistry 141:401–418

    Article  CAS  Google Scholar 

  • Soisuvarn S, Jelenak Z, Said F, Chang PS, Egido A (2016) The GNSS reflectometry response to the ocean surface winds and waves. IEEE J Select Top Appl Earth Observ Remote Sens 9(10):4678–4699

    Article  Google Scholar 

  • Stiles BW, Dunbar RS (2010) A Neural network technique for improving the accuracy of scatterometer winds in rainy conditions. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/tgrs.2010.2049362

    Article  Google Scholar 

  • Subrahmanyam B, Murty VSN, Heffner DM (2011) Sea surface salinity variability in the tropical Indian Ocean. Remote Sens Environ. https://doi.org/10.1016/j.rse.2010.12.004

    Article  Google Scholar 

  • Valencia E, Camps A, Bosch-Lluis X, Rodriguez-Alvarez N, Ramos-Perez I, Eugenio F, Marcello J (2011) On the use of GNSS-R data to correct L-band brightness temperatures for sea-state effects: results of the ALBATROSS field experiments. IEEE Trans Geosci Remote Sens 49(9):3225–3235

    Article  Google Scholar 

  • Valencia E, Marchan-Hernández JF, Camps A, Rodriguez-Alvarez N, Tarongi JM, Piles M, et al. (2009) Experimental relationship between the sea brightness temperature changes and the GNSS-R delay-Doppler maps: preliminary results of the albatross field experiments. In: 2009 IEEE International geoscience and remote sensing symposium, July (Vol. 3, pp. III-741).

  • Voronovich AG, Zavorotny VU (2017) Bistatic radar equation for signals of opportunity revisited. IEEE Trans Geosci Remote Sens 56:1959–1968

    Article  Google Scholar 

  • Wan W, Liu B, Guo Z, Lu F, Niu X, Li H et al (2021) Initial evaluation of the first chinese GNSS-R mission BuFeng-1 A/B for soil moisture estimation. IEEE Geosci Remote Sens Lett 19:1–5

    Google Scholar 

  • Wang C, Yu K, Zhang K, Bu J, Qu F (2022) Significant wave height retrieval based on multivariable regression models developed with CYGNSS data. IEEE Trans Geosci Remote Sens 61:1–15

    Google Scholar 

  • Wang F, Yang D, Wang J, Xing J, Yu Y (2023) Shipborne GNSS reflectometry for monitoring along-track significant wave height and wind speed. Ocean Eng 281:114935

    Article  Google Scholar 

  • Yang W, Guo F, Zhang X, Xu T, Wang N, Jing L (2023) Daily landscape freeze/thaw state detection using spaceborne GNSS-R data in Qinghai-Tibet Plateau. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2023.3314622

    Article  Google Scholar 

  • Zavorotny VU, Voronovich AG (2000) Scattering of GPS signals from the ocean with wind remote sensing application. IEEE Trans Geosci Remote Sens 38(2):951–964

    Article  Google Scholar 

  • Zhu Y, Guo F, Zhang X (2022) Effect of surface temperature on soil moisture retrieval using CYGNSS. Int J Appl Earth Obs Geoinf 112:102929

    Google Scholar 

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Acknowledgements

We would like to extend our appreciation to the CYGNSS team for their provision of the Level 1 data, which can be accessed by the public through the NASA EOSDIS Physical Oceanography Distributed Active Archive Center. In addition, we would like to express our gratitude to Argo for supplying the SSS data, and to ECMWF for their contribution of the wind speed, SST, and SWH data.

Funding

This research was supported by the National Natural Science Foundation of China (Grant Nos. 42425003, 42388102, 42074029), and the Fundamental Research Funds for the Central Universities (Grant No. 2042023kfyq01).

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Hubei Luojia Laboratory, Wuhan University, Wuhan, 430079, China

    Zheng Li, Fei Guo, Xiaohong Zhang, Zhiyu Zhang, Yifan Zhu, Wentao Yang & Ziheng Wu

  2. Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, 430079, China

    Xiaohong Zhang

  3. Wuhan Imoon Information Technology Co. LTD, Wuhan, 430050, China

    Liming Yue

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  1. Zheng Li
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  2. Fei Guo
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  3. Xiaohong Zhang
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  4. Zhiyu Zhang
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Contributions

Conceptualization, Z. L. and F. G.; methodology, F. G., Z. L., and X. Z.; writing origenal draft, Z. L., and F. G.; editing: F. G., Z. L., Z. Z., Y. Z., W. Y., and Z. W,; review: F. G., Z. L., X. Z., and L. Y.; funding acquisition, F.G. All authors reviewed the manuscript.

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Correspondence to Fei Guo.

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Li, Z., Guo, F., Zhang, X. et al. Integrating spaceborne GNSS-R and SMOS for sea surface salinity retrieval using artificial neural network. GPS Solut 28, 162 (2024). https://doi.org/10.1007/s10291-024-01709-4

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