• Anterrieu, E., , and Camps A. , 2008: On the reduction of the systematic error in imaging radiometry by aperture synthesis: A new approach for the SMOS space mission. Microwave Radiometry and Remote Sensing of the Environment: Proc. MicroRad, Florence, Italy, IEEE, doi:10.1109/MICRAD.2008.4579462.

    • Search Google Scholar
    • Export Citation
  • Barlow, R., 1989: Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences. The Manchester Physics Series, John Wiley & Sons, 204 pp.

    • Search Google Scholar
    • Export Citation
  • Barré, H. M. J. P., , Duesmann B. , , and Kerr Y. H. , 2008: SMOS: The mission and the system. IEEE Trans. Geosci. Remote Sens., 46, 587593.

    • Search Google Scholar
    • Export Citation
  • Butora, R., , and Camps A. , 2003: Noise maps in aperture synthesis radiometric images due to cross correlation of visibility noise. Radio Sci., 38, 1067, doi:10.1029/2002RS002707.

    • Search Google Scholar
    • Export Citation
  • Camps, A., , Bará J. , , Corbella I. , , and Torres F. , 1997: The processing of hexagonally sampled signals with standard rectangular techniques: Application to 2D large aperture synthesis interferometric radiometers. IEEE Trans. Geosci. Remote Sens., 35, 183190.

    • Search Google Scholar
    • Export Citation
  • Camps, A., , Corbella I. , , Vall-llossera M. , , Duffo N. , , Marcos F. , , Martínez-Fadrique F. , , and Greiner M. , 2003: The SMOS end-to-end performance simulator: Description and scientific applications. Proc. IEEE Int. Geoscience and Remote Sensing Symp.: IGARSS ‘03, Vol. 1, Toulouse, France, IEEE, 13–15.

    • Search Google Scholar
    • Export Citation
  • Camps, A., , Vall-llossera M. , , Batres L. , , Torres F. , , Duffo N. , , and Corbella I. , 2005: Retrieving sea surface salinity with multiangular L-band brightness temperatures: Improvement by spatiotemporal averaging. Radio Sci., 40, RS2003, doi:10.1029/2004RS003040.

    • Search Google Scholar
    • Export Citation
  • Camps, A., , Vall-llossera M. , , Corbella I. , , Duffo N. , , and Torres F. , 2008: Improved image reconstruction algorithm for aperture synthesis radiometers. IEEE Trans. Geosci. Remote Sens., 46, 146158.

    • Search Google Scholar
    • Export Citation
  • Font, J., , Lagerloef G. , , LeVine D. , , Camps A. , , and Zanife O. Z. , 2004: The determination of surface salinity with the European SMOS space mission. IEEE Trans. Geosci. Remote Sens., 42, 21962205.

    • Search Google Scholar
    • Export Citation
  • Font, J., , Gabarró C. , , and Mourre B. , 2005: Synergetic aspects and auxiliary data concepts for sea surface salinity measurements from space. WP4000: Summary, Synthesis and Recommendations—Final Report, ESA ESTEC 18176/04/NL/CB, 613 pp.

    • Search Google Scholar
    • Export Citation
  • Font, J., , Camps A. , , and Ballabrera-Poy J. , 2008: Microwave aperture synthesis radiometry: Setting the path for (operational) sea salinity measurement from space. Remote Sensing of European Seas, V. Barale and M. Gade, Eds., Springer, 223–238.

    • Search Google Scholar
    • Export Citation
  • Gabarró, C., , Portabella M. , , Talone M. , , and Font J. , 2009: Toward an optimal SMOS ocean salinity inversion algorithm. IEEE Geosci. Remote Sens. Lett., 6, 509513.

    • Search Google Scholar
    • Export Citation
  • Hollinger, J. P., 1971: Passive microwave measurements of the sea surface roughness. IEEE Trans. Geosci. Electron., 9, 165169.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471.

  • Klein, L. A., , and Swift C. T. , 1977: An improved model for the dielectric constant of sea water at microwave frequencies. IEEE Trans. Antennas Propag., 25, 104111.

    • Search Google Scholar
    • Export Citation
  • Levitus, S., and Coauthors, 1998: World Ocean Database. NOAA/NESDIS 18, 346 pp.

  • Madec, G., 2008: NEMO ocean engine version 3. Note du Pôle de modélisation de l’Institut Pierre-Simon Laplace 27, 217 pp.

  • McMullan, K. D., , Brown M. A. , , Martín-Neira M. , , Rits W. , , Ekhlom S. , , Marti J. , , and Lemanczyk J. , 2008: SMOS: The payload. IEEE Trans. Geosci. Remote Sens., 46, 594605.

    • Search Google Scholar
    • Export Citation
  • Mourre, B., , Ballabrera-Poy J. , , García-Ladona E. , , and Font J. , 2008: Surface salinity response to changes in the model parameters and forcings in a climatological simulation of the eastern North Atlantic Ocean. Ocean Modell., 23, 2132.

    • Search Google Scholar
    • Export Citation
  • Randa, J., and Coauthors, 2008: Recommended terminology for microwave radiometry. NIST Tech. Note 151, 40 pp.

  • Rodgers, J. L., , and Nicewander W. A. , 1988: Thirteen ways to look at the correlation coefficient. Amer. Stat., 42, 5966.

  • Sabia, R., , Camps A. , , Vall-llossera M. , , and Reul N. , 2006: Impact on sea surface salinity retrieval of different auxiliary data within the SMOS mission. IEEE Trans. Geosci. Remote Sens., 44, 27692778.

    • Search Google Scholar
    • Export Citation
  • Sabia, R., , Camps A. , , Talone M. , , Vall-llossera M. , , and Font J. , 2010: Determination of the sea surface salinity error budget in the soil moisture and ocean salinity mission. IEEE Trans. Geosci. Remote Sens., 48, 16841693.

    • Search Google Scholar
    • Export Citation
  • SEPS, 2006a: SMOS End-to-End Performance Simulator (SEPS) Architectural and Detailed Design Document (ADDD). Version 5, ESA, 226 pp. [Available online at http://tarod.cmima.csic.es/alfresco/d/a/workspace/SpacesStore/1a95fa59-eaa2-499b-b41a-89c053737182/ADDD_v5.0.pdf.]

    • Search Google Scholar
    • Export Citation
  • SEPS, 2006b: SEPS software user manual (SUM). Version 4.0, ESA, 276 pp. [Available online at http://tarod.cmima.csic.es/alfresco/d/a/workspace/SpacesStore/98542501-adde-424d-9955-fc68207af1e4/SUM_v4.0.pdf.]

    • Search Google Scholar
    • Export Citation
  • Snyder, J. P., 1992: An equal-area map projection for polyhedral globes. Cartographica, 29, 1021.

  • Suess, M., , Matos P. , , Gutierrez A. , , Zundo M. , , and Martin-Neira M. , 2004: Processing of SMOS level 1c data onto a discrete global grid. Proc. IEEE Int. Geoscience and Remote Sensing Symp., IGARSS ‘04, Anchorage, AK, IEEE, 1914–1917.

    • Search Google Scholar
    • Export Citation
  • Torres, F., , Corbella I. , , Camps A. , , Duffo N. , , and Vall-llossera M. , 2005: Error budget map to SRD (System Requirements Document) PRS. Project: Image Validation Support and SEPS Development, Validation and Delivery for SMOS PLM, Phase C/D, ESA Doc. Ref. SO-TN-UPCPLM-0007, version 7.0.

    • Search Google Scholar
    • Export Citation
  • Talone, M., , Camps A. , , Sabia R. , , and Font J. , 2007: Towards a coherent sea surface salinity product from SMOS radiometric measurements and Argo buoys. Proc. IEEE Int. Geoscience and Remote Sensing Symp., IGARSS 2007, Barcelona, Spain, IEEE, 3959–3962.

    • Search Google Scholar
    • Export Citation
  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 29613012.

  • Zine, S., and Coauthors, 2008: Overview of the SMOS sea surface salinity prototype processor. IEEE Trans. Geosci. Remote Sens., 46, 621645.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 7 7 1
PDF Downloads 2 2 1

Error Covariance Matrices Characterization in the Ocean Salinity Retrieval Cost Function within the SMOS Mission

View More View Less
  • 1 Remote Sensing Laboratory, Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya, and IEEC CRAE/UPC, and SMOS-Barcelona Expert Centre, Barcelona, Spain
  • | 2 Institut de Ciències del Mar-CSIC, and SMOS-Barcelona Expert Centre, Barcelona, Spain
© Get Permissions
Restricted access

Abstract

The interests of the scientific community working on the Soil Moisture and Ocean Salinity (SMOS) ocean salinity level 2 processor definition are currently focused on improving the performance of the retrieval algorithm, which is based on an iterative procedure where a cost function relating models, measurements, and auxiliary data is minimized. For this reason, most of the effort is currently focused on the analysis and the optimization of the cost function.

Within this framework, this study represents a contribution to the assessment of one of the pending issues in the definition of the cost function: the optimal weight to be given to the radiometric measurements with respect to the weight given to the background geophysical terms.

A whole month of brightness temperature acquisitions have been simulated by means of the SMOS-End-to-End Performance Simulator. The level 2 retrieval has been performed using the Universitat Politècnica de Catalunya (UPC) level 2 processor simulator using four different configurations, namely, the direct covariance matrices, the two cost functions currently described in the SMOS literature, and, finally, a new weight (the so-called effective number of measurement).

Results show that not even the proposed weight properly drives the minimization, and that the current cost function has to be modified in order to avoid the introduction of artifacts in the retrieval procedure. The calculation of the brightness temperature misfit covariance matrices reveals the presence of very complex patterns, and the inclusion of those in the cost function strongly modifies the retrieval performance. Worse but more Gaussian results are obtained, pointing out the need for a more accurate modeling of the correlation between brightness temperature misfits, in order to ensure a proper balancing with the relative weights to be given to the geophysical terms.

Current affiliation: Serco SpA, Frascati, Italy.

Current affiliation: European Space Agency-ESRIN, Frascati, Italy.

Corresponding author address: M. Talone, Via Sciadonna, 24, Serco SpA, 00044 Frascati, Italy. E-mail: talone@tsc.upc.edu

Abstract

The interests of the scientific community working on the Soil Moisture and Ocean Salinity (SMOS) ocean salinity level 2 processor definition are currently focused on improving the performance of the retrieval algorithm, which is based on an iterative procedure where a cost function relating models, measurements, and auxiliary data is minimized. For this reason, most of the effort is currently focused on the analysis and the optimization of the cost function.

Within this framework, this study represents a contribution to the assessment of one of the pending issues in the definition of the cost function: the optimal weight to be given to the radiometric measurements with respect to the weight given to the background geophysical terms.

A whole month of brightness temperature acquisitions have been simulated by means of the SMOS-End-to-End Performance Simulator. The level 2 retrieval has been performed using the Universitat Politècnica de Catalunya (UPC) level 2 processor simulator using four different configurations, namely, the direct covariance matrices, the two cost functions currently described in the SMOS literature, and, finally, a new weight (the so-called effective number of measurement).

Results show that not even the proposed weight properly drives the minimization, and that the current cost function has to be modified in order to avoid the introduction of artifacts in the retrieval procedure. The calculation of the brightness temperature misfit covariance matrices reveals the presence of very complex patterns, and the inclusion of those in the cost function strongly modifies the retrieval performance. Worse but more Gaussian results are obtained, pointing out the need for a more accurate modeling of the correlation between brightness temperature misfits, in order to ensure a proper balancing with the relative weights to be given to the geophysical terms.

Current affiliation: Serco SpA, Frascati, Italy.

Current affiliation: European Space Agency-ESRIN, Frascati, Italy.

Corresponding author address: M. Talone, Via Sciadonna, 24, Serco SpA, 00044 Frascati, Italy. E-mail: talone@tsc.upc.edu
Save