1. Universitat Politècnica de Catalunya CORRECTION OF SPATIAL ERRORS IN SMOS BRIGHTNESS TEMPERATURE IMAGES L. Wu, I. Corbella, F. Torres, N. Duffo, M. Martín-Neira

2. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada2/20 SMOS Image Reconstruction Errors All visibility samples show residual systematic errors due to non-perfect instrument calibration. –PMS gain (residual Tph dependence) –PMS offset (heater) –Gkj phase (LO rate) The Flat Target response, as a set of Visibilities, is prone to the same errors. Uncertainties in antenna patterns are directly inherited by the G-matrix elements. As a result: The reconstructed Brightness temperature shows spatial distortion in the ξ,η plane This error is systematic and cannot be reduced by time averaging.

3. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada3/20 Spatial errors model At each snapshot MIRAS delivers a Brightness Temperature image T B (ξ,η). Spatial artifacts can be modeled as having –Different “gains” at each grid point –Different “offsets” at each grid point The first option is considered here as offset is highly cancelled by the “Flat Target Transformation”

4. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada4/20 Estimating the spatial error Spatial error is estimated by carefully analyzing a large number of measurements over a constant target (i.e. the ocean). To minimize temporal and geophysical variations, a large number of orbits at different dates are used. Ascending and descending orbits are considered. The final estimation is an image independent mask to be applied to all measurements.

5. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada5/20 Basics on mask estimation over the ocean Geometry (faraday) rotation Polynomial regression Geometry (faraday) rotation The final mask is computed on antenna frame.

6. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada6/20 Polynomial regression Measured brightness temperature at different (ξ-η) is converted to ground plane and arranged for equal incidence angle. Geophysical variability within the FOV of a single snapshot is neglected. Spatial errors are computed as the difference between the measured brightness temperature and its estimation by polynomial regression.

7. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada7/20 Mask estimation Brightness temperature absolute errors at ground frame are estimated by the regression Errors are transformed to instrument frame and converted to relative The mask is computed from the estimated relative errors as: Once the mask is available, the corrected TB is:

8. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada8/20 Faraday rotation angle Faraday rotation is stronger when the TEC (Total Electron Content) of the atmosphere is larger Descending orbits Spring and Fall

9. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada9/20 Faraday rotation correction Faraday rotation correction must be applied. Difference between ascending and descending orbits, before (left) and after (right) faraday rotation correction.

10. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada10/20 Final Mask on antenna frame Mask is computed using several orbits over the pacific ocean from 20 th,Feb to 20 th,Sep, 2010.

11. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada11/20 Cuts of the mask X pol Y pol The spatial error is constrained to ±2% in SMOS AF-FoV

12. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada12/20 Mask average along track Mask average along track is not zero mean producing artifacts along this direction.

13. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada13/20 Images over the Indian ocean (01/12) applying the mask

14. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada14/20 Residual Spatial errors case Error budget v7.0 2.13 Before mask correction 1.341.57 After mask correction 0.380.47 Radiometric spatial errors (pixel bias) over ocean The mask clearly reduces the residual error and randomizes its spatial distribution.

15. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada15/20 Corrected brightness temperature H/V brightness temperature arranged for equal incidence angle for a SMOS image over the Atlantic Ocean and the Indian Ocean, before (top) and after (bottom) applying the mask correction.

16. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada16/20 Effect on Level 1C brightness temperature Horizontal polarization Vertical polarization (35º to 55º incidence)

17. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada17/20 Image cut at -25º latitude

18. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada18/20 Mask stability check (i) single mask deviation from mean mask computed in March, May and July 2010 to show temporal stability.

19. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada19/20 Mask stability check (ii) the masks computed using ascending orbits' data are more stable than using descending orbits'.

20. Universitat Politècnica de Catalunya 28th July 2011IGARSS 2011. Vancouver. Canada20/20 Conclusions Systematic image reconstruction spatial errors have been estimated from a large number of observations over the ocean. A method to correct for these errors has been developed, with special application to improve salinity retrievals. Correction is as simple as multiplying the raw measurements at level 1B by a constant, direction dependent “mask”. The mask computation is affected by Faraday rotation correction. Results both in L1B and L1C data show that there is a reduction of the artifacts.