2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font


  • Antonio Turiel Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Maria Piles Barcelona Expert Centre. Institute of Marine Sciences, CSIC - Signal and Communications Theory Department, Universitat Politècnica de Catalunya
  • Verónica González-Gambau Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Joaquim Ballabrera-Poy Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Carolina Gabarró Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Justino Martinez Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Estrella Olmedo Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Marcos Portabella Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Fernando Pérez Barcelona Expert Centre. Institute of Marine Sciences, CSIC
  • Jordi Solé Barcelona Expert Centre. Institute of Marine Sciences, CSIC




SMOS, salinity, ocean circulation, oceanography, soil moisture, sea ice, radiometry, remote sensing


Soil Moisture and Ocean Salinity (SMOS) is the first satellite mission capable of measuring sea surface salinity and soil moisture from space. Its novel instrument (the L-band radiometer MIRAS) has required the development of new algorithms to process SMOS data, a challenging task due to many processing issues and the difficulties inherent in a new technology. In the wake of SMOS, a new community of users has grown, requesting new products and applications, and extending the interest in this novel brand of satellite services. This paper reviews the role played by the Barcelona Expert Centre under the direction of Jordi Font, SMOS co-principal investigator. The main scientific activities and achievements and the future directions are discussed, highlighting the importance of the oceanographic applications of the mission.


Download data is not yet available.


Antonov J.I., Seidov D., Boyer T.P., et al. 2010. World Ocean Atlas 2009, Salinity. In: Levitus S. (ed.) NOAA Atlas NESDIS 69 Vol. 2, U.S. Government Printing Office, Washington, D.C. 184 pp. PMid:22184514 PMCid:PMC3222265

Argo. 2013. Argo quality control manual. V2. 9: 54 pp.

Ballabrera J., Hoareau N., Umbert M., et al. 2013. Tropical Pacific sea surface salinity variability derived from SMOS data: Comparison with in-situ observations. EGU General Assembly Conference Abstracts 15: 4928.

Boutin J., Martin N., Yin X., et al. 2012. First assessment of SMOS data over open ocean. Part II – Sea surface salinity. IEEE Trans. Geosci. Remote Sens. 50(5): 1662-1675. http://dx.doi.org/10.1109/TGRS.2012.2184546

Blower J., Haines K., Santokhee A., et al. 2009. Godiva2: Interactive visualization of environmental data on the web. Phil. Trans. Roy. Soc. A, 367: 1035-1039. http://dx.doi.org/10.1098/rsta.2008.0180 PMid:19087942

Bretherton F.P., Davis R.E., Fandry C.B. 1976. A technique for objective analysis and design of oceanographic experiments applied to MODE-73. Deep-Sea Res. 23: 559-582. http://dx.doi.org/10.1016/0011-7471(76)90001-2

Camps A., Font J., Vall-llossera M., et al. 2004. The WISE 2000 and 2001 Campaigns in Support of the SMOS Mission: Sea Surface L-band Brightness Temperature Observations and their Application to Multi-Angular Salinity Retrieval. IEEE Trans. Geosci. Remote Sens. 42: 804-823. http://dx.doi.org/10.1109/TGRS.2003.819444

Camps A., Gabarró C., Vall-llossera M., et al. 2016. From field experiments to salinity products: a tribute to the contributions of Jordi Font to the SMOS mission. Sci. Mar. 80S1: 159-172.

Chaparro D., Vayreda J., Martinez-Vilalta J., et al. 2014. SMOS and climate data applicability for analyzing forest decline and forest fires. Geosci. Remote Sens. Symp. (IGARSS). 2014 IEEE Int. 13-18 July 2014. 1069-1072. http://dx.doi.org/10.1109/igarss.2014.6946613

Chaparro D., Vall-llossera M., Piles M., et al. 2015. Low Soil Moisture and High Temperatures as indicators for Forest Fire Ocurrence and Extent across the Iberian Peninsula. Geosci. Remote Sens. Symp. (IGARSS). 2015 IEEE Int. 26-31 July 2015. 3325-3328.

Comiso J.C., Cavalieri D.J., Markus T. 2003. Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans. Geosci. Remote Sens. 41(2): 243-252. http://dx.doi.org/10.1109/TGRS.2002.808317

Corbella I., Torres F., Camps A., et al. 2005. MIRAS end-to-end calibration: application to SMOS L1 processor. IEEE Trans. Geosci. Remote Sens. 43: 1126-1134. http://dx.doi.org/10.1109/TGRS.2004.840458

Corbella I., Torres F., Duffo N., et al. 2011. MIRAS Calibration and Performance: Results From the SMOS In-Orbit Commissioning Phase. IEEE Trans. Geosci. Remote Sens. 49: 3147-3155. http://dx.doi.org/10.1109/TGRS.2010.2102769

Corbella I., Torres F., Wu L., et al. 2013. Spatial Biases Analysis and Mitigation Methods in SMOS Images. Geosci. Remote Sens. Symp. (IGARSS). 2013 IEEE Int. 21-26 July 2013. 3415-3418. http://dx.doi.org/10.1109/igarss.2013.6723562

Corbella I., Duran I., Wu L., et al. 2015. Impact of Correlator Efficiency Errors on SMOS Land–Sea Contamination. IEEE Geosci. Remote Sens. Lett. 99: 1-5. http://dx.doi.org/10.1109/lgrs.2015.2428653

Crapolicchio R. 2008. TEC usage for the SMOS Level 1 Operational Processor. Tech. rept. XSMS-GSEG-EOPG-TN-06-0019.

Daganzo-Eusebio E., Oliva R., Kerr Y., et al. 2013. SMOS radiometer in the 1400-1427 MHz passive band: Impact of the RFI environment and approach to its mitigation and cancellation. IEEE Trans. Geosci. Remote Sens. 51: 4999-5007. http://dx.doi.org/10.1109/TGRS.2013.2259179

Entekhabi D., Njoku E., O'Neill P., et al. 2010. The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE. 98: 704-716. http://dx.doi.org/10.1109/JPROC.2010.2043918

Font J., Camps A., Borges A., et al. 2010a. SMOS: The challenging Sea Surface Salinity Measurement from Space. Proc. IEEE. 98: 649-665. http://dx.doi.org/10.1109/JPROC.2009.2033096

Font J., Boutin J., Reul N., et al. 2010b. SMOS objectives and approach for ocean salinity observations. Proc. ESA Liv. Planet Symp., Bergen, Norway, Jun. 28-Jul. 2, 2010, ESA SP-686.

Gabarró C., Font J., Camps A., et al. 2004. A new empirical model of the sea surface microwave emissivity for the salinity remote sensing. Geoph. Res. Lett. 31.

Gabarró C., Font J., Miller J., et al. 2008. The usage of a semi-empirical emissivity model for a rough estimation of sea surface salinity from an airborne microwave radiometer. Sci. Mar. 72: 329-336.

Gabarró C., Portabella M., Talone M., et al. 2009. Towards an Optimal SMOS Ocean Salinity Inversion Algorithm. Geosci. Remote Sens. 6: 509-513. http://dx.doi.org/10.1109/LGRS.2009.2018490

Gabarró C., González-Gambau V., Corbella I., et al. 2013. Impact of the Local Oscillator Calibration Rate on the SMOS Measurements and Retrieved Salinities. IEEE Trans. Geosci. Remote Sens. 51(9): 4633-4642. http://dx.doi.org/10.1109/TGRS.2012.2233744

Gabarró C., Pla Q., Elosegui P., et al. 2015. Investigating SMOS data for sea ice concentration determinatation. SMOS Sci.Work.

González-Gambau V., Corbella I., Torres F., et al. 2014. Latitudinal and seasonal SMOS amplitude calibration assessment. Geosci. Remote Sens. Symp. (IGARSS). 2014 IEEE Int. 13-18 July 2014. 1917-1920. http://dx.doi.org/10.1109/igarss.2014.6946833

González-Gambau V., Turiel A., Olmedo E., et al. 2016. Nodal Sampling: A New Image Reconstruction Algorithm for SMOS. IEEE Trans. Geosci. Remote Sens. 54: 2314-2328. http://dx.doi.org/10.1109/TGRS.2015.2499324

González-Zamora A., Sánchez N., Martínez-Fernández J., et al. 2015. Long-term SMOS soil moisture products: a comprehensive evaluation across scales and methods in the Duero Basin (Spain). J. Phys. Chem. Earth. 83-84: 123-136. http://dx.doi.org/10.1016/j.pce.2015.05.009

Gourrion J., Guimbard S., Portabella M., et al. 2013. Toward an Optimal Estimation of the SMOS Antenna-Frame Systematic Errors. IEEE Trans. Geosci. Remote Sens. 51(9): 4752-4760. http://dx.doi.org/10.1109/TGRS.2013.2271593

Grodsky S.A., Reul N., Lagerloef G., et al. 2012. Haline hurricane wake in the Amazon/Orinoco plume: AQUARIUS/SACD and SMOS observations. Geoph. Res. Lett. 39: L20603.

Guimbard S., Gourrion J., Portabella M., et al. 2012. SMOS Semi- Empirical Ocean Forward Model Adjuntment. IEEE Trans. Geosci. Remote Sens. 50(5): 1676-1687. http://dx.doi.org/10.1109/TGRS.2012.2188410

Huntemann M., Heygster G., Kaleschke L., et al. 2013. Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations. The Cryosphere 8: 439-451. http://dx.doi.org/10.5194/tc-8-439-2014

Jordà G., Gomis D. 2009. Towards SMOS L4 SSS products: Improving L3 SSS with auxiliary SSS data. IEEE Trans. Geosci. Remote Sens. 48: 2204-2214. http://dx.doi.org/10.1109/TGRS.2009.2037899

Kaleschke L., Lupkes C., Vihma T., et al. 2001. SSM/I Sea ice remote sensing for mesoscale ocean–atmosphere interaction analysis. Can. J. Remote Sens. 27(5): 526-537. http://dx.doi.org/10.1080/07038992.2001.10854892

Kaleschke L., Maaß N., Haas C., et al. 2010. A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice. The Cryosphere 4: 583-592. http://dx.doi.org/10.5194/tc-4-583-2010

Kaleschke L., Tian-Kunze X., Maaß N., et al. 2012. Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period. Geoph. Res. Lett. 39: L05501.

Kerr Y., Waldteufel P., Wigneron J.P., et al. 2010. The SMOS mission: New Tool for Monitoring Key Elements of the Global Water Cycle. Proc. IEEE. 98: 666-687. http://dx.doi.org/10.1109/JPROC.2010.2043032

Klein L., Swift C. 1977. An Improved Model for the Dielectric Constant of Sea Water at Microwave Frequencies. IEEE Trans. Ant. Prop. 25: 104-111. http://dx.doi.org/10.1109/TAP.1977.1141539

L1 team. 2015. Definition of a metric to assess SMOS L1 Data Quality, SM-TN-AURO-L1OP-0003, ESA Tech. rept.

Le Vine D.M., Lagerloef G.S.E., Colomb F.R., Yueh S.H., Pellerano F.A. 2007. Aquarius: An Instrument to Monitor Sea Surface Salinity From Space. IEEE Trans. Geosci. Remote Sens. 45(7): 2040-2050. http://dx.doi.org/10.1109/TGRS.2007.898092

McCulloch M.E., Spurgeon P., Chuprin A. 2011. Have mid-latitude ocean rain-lenses been seen by the SMOS satellite? Ocean Model. 43: 108-111.

McMullan K.D., Brown M.A., Martín-Neira M., et al. 2008. SMOS: the payload. IEEE Trans. Geosci Remote Sens. 46(3): 594-605. http://dx.doi.org/10.1109/TGRS.2007.914809

Meirold-Mautner I., Mugerin C., Vergely J., et al. 2009. SMOS ocean salinity performance and TB bias correction. EGU Gen. Assem.

Oliva R., Daganzo E., Kerr Y., et al. 2012. SMOS Radio Frequency Interference scenario: Status and actions taken to improve the RFI environment in the 1400-1427 MHz passive band. IEEE Trans. Geosci. Remote Sens. 50(5): 1427-1439. http://dx.doi.org/10.1109/TGRS.2012.2182775

Oliva R., Martín-Neira M., Corbella I., et al. 2013. SMOS calibration and instrument performance after one year in orbit. IEEE Trans. Geosci. Remote Sens. 51(1): 654-670. http://dx.doi.org/10.1109/TGRS.2012.2198827

Park H., González-Gambau V., Camps A. 2015. High angular resolution RFI localization in synthetic aperture interferometric radiometers using Direction of Arrival estimation. IEEE Geosci. Remote Sens. Lett. 12(1): 102-106. http://dx.doi.org/10.1109/LGRS.2014.2327006

Piles M., Camps A., Vall-llossera M., et al. 2011. Downscaling SMOS-derived soil moisture using MODIS visible/infrared data. IEEE Trans. Geosci. Remote Sens. 49: 3156-3166. http://dx.doi.org/10.1109/TGRS.2011.2120615

Piles M., Vall-llossera M., Camps A., et al. 2013. On the synergy of SMOS and Terra/Aqua MODIS: high resolution soil moisture maps in near real-time. Geosci. Remote Sens. Symp. 2013 IEEE Int. 21-26 July 2013. 3423-3426.

Piles M., Sánchez N., Vall-llossera M., et al. 2014. A downscaling approach for SMOS land observations: two year evaluation of high resolution soil moisture maps over the Iberian Peninsula. IEEE J. Sel. Top. App. Earth Observ. Remote Sens. 7(9): 3845-3857. http://dx.doi.org/10.1109/JSTARS.2014.2325398

Piles M., Pou X., Camps A., et al. 2015. Quality report: validation of SMOS-BEC L4 high resolution soil moisture products, version 3.0 or "all-weather", Tech. rept. http://cp34-bec.cmima.csic.es/doc/BEC-SMOS-L4SMv3-QR. pdf.

Piles M., Martínez E., Ballabrera J., et al. In press. Estimation of global soil moisture seasonal variability using SMOS satellite observations. Proc. Rec. Adv. Quant. Remote Sens. 396-400.

Reul N., Tenerelli J., Chapron B., et al. 2007. Modeling Sun Glitter at L-Band for Sea Surface Salinity Remote Sensing With SMOS. IEEE Trans. Geosci. Remote Sens. 45: 2073-2087. http://dx.doi.org/10.1109/TGRS.2006.890421

Reul N., Tenerelli J., Chapron B., et al. 2012. SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes. J. Geophys. Res. 117: C02006. http://dx.doi.org/10.1029/2011JC007474

Reul N., Quilfen Y., Chapron B., et al. 2014. Multisensor observations of the Amazon-Orinoco river plume interactions with hurricanes. J. Geophys. Res. Oceans. 119: 8271-8295. http://dx.doi.org/10.1002/2014JC010107

Sánchez N., Piles M., Martínez-Fernández J., et al. 2014. Hyperspectral Optical, Thermal and Microwave L-band Observations for Soil Moisture Retrieval at Very High Spatial Resolution. Photogram. Eng. Remote Sens. 80: 745-755. http://dx.doi.org/10.14358/PERS.80.8.745

Sánchez N., Alonso-Arroyo A., Martínez-Fernández J., et al. 2015. On the Synergy of Airborne GNSS-R and Landsat 8 for Soil Moisture Estimation. Remote Sens. 7: 9954-9974. http://dx.doi.org/10.3390/rs70809954

Sánchez-Ruiz S., Piles M., Sánchez N., et al. 2014. Combining SMOS with visible and near/shortwave/thermal infrared satellite data for high resolution soil moisture estimates. J. Hydrol. 49: 3156-3166 http://dx.doi.org/10.1016/j.jhydrol.2013.12.047

Sánchez-Ruiz S., Moreno A., Martínez B., et al. In press. Impact of water stress on GPP estimation from remote sensing data in Mediterranean ecosystems. Proc. Rec. Adv. Quant. Remote Sens. pp. 338-343.

Slominska E., Gabarro C., Marczewski W., et al. 2015. Observations of Antarctic icebergs acquired with the SMOS satellite. SMOS Sci. Work.

Stoffelen A. 1998. Toward the true near surface wind speed: Error modeling and calibration using triple collocation. J. Geophys. Res. 103: 7755-7766. http://dx.doi.org/10.1029/97JC03180

Talone M., Camps A., Mourre B., et al. 2009. Simulated SMOS Level 2 and 3 Products: the Effect of Introducing ARGO Data in the Processing Chain and its Impact on the Error Induced by the Vicinity of the Coast. IEEE Trans. Geosci. Remote Sens. 47: 3041-3050. http://dx.doi.org/10.1109/TGRS.2008.2011618

Talone M., Sabia R., Camps A., et al. 2010. Sea surface salinity retrievals from HUT-2D L-band radiometric measurements. Remote Sens. Environ. 114:1756-1764. http://dx.doi.org/10.1016/j.rse.2010.03.006

Talone M., Gabarró C., Camps A., et al. 2011. Error covariance matrices characterization in the ocean salinity retrieval cost function within the SMOS mission. J. Atmosp. Oce. Tech. 28: 1155-1166. http://dx.doi.org/10.1175/2011JTECHO813.1

Tenerelli J., Reul N. 2010. Analysis of L1PP Calibration Approach Impact in SMOS TBs and 3-Days SSS Retrievals over the Pacific Using an Alternative Ocean Target Transformation Ap plied to L1OP Data. Tech. rept. IFREMER/CLS.

Tenerelli J., Reul N., Mouche A., et al. 2008. Earth-Viewing L-Band Radiometer Sensing of Sea Surface Scattered Celestial Sky Radiation—Part I: General Characteristics. IEEE Trans. Geosci. Remote Sens. 46: 659-674. http://dx.doi.org/10.1109/TGRS.2007.914803

Thébault E., Finlay C., Beggan C., et al. 2015. International Geomagnetic Reference Field: the 12th generation. Earth, Planets and Space. 67: 79. http://dx.doi.org/10.1186/s40623-015-0228-9

Torres F., Camps A., Bara J., et al. 1996. On-board phase and modulus calibration of large aperture synthesis radiometers: Study applied to MIRAS. IEEE Trans. Geosci. Remote Sens. 34: 1000-1009. http://dx.doi.org/10.1109/36.508417

Torres F., González-Gambau V., González-Haro C. 2008. One-point calibration in interferometric radiometers devoted to Earth observation. Proc. SPIE Europe Remote Sens.

Turiel A., Yahia H., Pérez-Vicente C.J. 2008. Microcanonical Multifractal Formalism: a geometrical approach to multifractal systems. Part I: Singularity Analysis. J. Phys. A. 41: 015501. http://dx.doi.org/10.1088/1751-8113/41/1/015501

Turiel A., Nieves V., García-Ladona E., et al. 2009. The multifractal structure of satellite temperature images can be used to obtain global maps of ocean currents. Oce. Sci. 5: 447-460. http://dx.doi.org/10.5194/os-5-447-2009

Umbert M., Hoareau N., Turiel A., et al. 2014. New blending algorithm to synergize ocean variables: The case of SMOS sea surface salinity maps. Remote Sens. Environ. 146: 172-187. http://dx.doi.org/10.1016/j.rse.2013.09.018

Umbert M., Guimbard S., Lagerloef G., et al. 2015. Detecting the surface salinity signature of Gulf Stream cold-core rings in Aquarius synergistic products. J. Geoph. Res. 120: 859-874. http://dx.doi.org/10.1002/2014JC010466

Zine S., Boutin J., Font J., et al. 2008. Overview of the SMOS sea surface salinity prototype processor. IEEE Trans. Geosci. Remote Sens. 46: 621-645. http://dx.doi.org/10.1109/TGRS.2008.915543



How to Cite

Turiel A, Piles M, González-Gambau V, Ballabrera-Poy J, Gabarró C, Martinez J, Olmedo E, Portabella M, Pérez F, Solé J. 2000 days of SMOS at the Barcelona Expert Centre: a tribute to the work of Jordi Font. scimar [Internet]. 2016Sep.30 [cited 2023Dec.3];80(S1):173-9. Available from: https://scientiamarina.revistas.csic.es/index.php/scientiamarina/article/view/1667




Most read articles by the same author(s)