The polarised brightness temperature for each incidence angle θis modelled as the sum of the theoretical model for flat surfaceemission and a term dependent on

surface roughness, parameterised through wind speed (WS) and / or significant wave height (SWH).

Two empirical models for ∆TB_rough fitted from WISE measurements were derived:

Sea Surface Temperature data:

TB is highly non-linearly dependent on SST. Figure 3 shows the distribution of the cost function without restrictions. A direct and unique minimum is not visible in this case.

Figure 4 shows that considering restrictions a clear minimum appears, so it is possible to find it when the possible solutions are restricted.

The results after retrieving SSS, WS, SWH and SST from radiometer data appear to be a little bit better than for the previous case:

σ2tb=1, σ2

SSS=0.25, σ2WS=4 , σ2

swh=0.25 σ2sst=1.0 σ2

tb=1, σ2SSS=1.0, σ2

WS=6.25 σ2swh=1.0 σ2

sst=1.5

|SSS in situ - SSS ret| = 0.20 psu |SSS in situ - SSS ret| = 0.25 psu |WS in situ - WS ret| = 1.05 m/s |WS in situ - WS ret| = 1.13 m/s|SWH in situ - SWH ret| = 0.28 m |SWH in situ - SWH ret| = 0.48 m|SST in situ - SSTret| = 0.20 ºC |SST in situ - SSTret| = 0.64 ºC

ACKNOWLEGMENT This study was funded by ESA-ESTEC under WISE contract 14188/00/NL/DC and by the Spanish National Program

on Space Research grants MIDAS ESP2001-4523-PE and ESP2002-11604-E. The authors very much appreciate the cooperation of Puertos del Estado for providing us with the HIRLAM and WAM data for the periods required.

Conclusions:Salinity is retrieved better when WS, SWH and SST are determined from the radiometric data than using the available numerical models (WS, SWH) or poor quality data (SST).

IV. How to obtain the auxiliary data?

A new semi-empirical model of sea surface microwave emissivity used to retrieve salinity, wind speed and wave height

III. Semi-Empirical Emissivity Models from WISE Experiment

Conclusion: The WISE model with mixed dependence provides the best salinity retrieval in this case.

),(),,( ,_,_, RTSSSSSTTT proughBpflatBpB θθ ∆+=

⋅−⋅≈∆⋅+⋅≈∆WSTWST

v

h

)º50/1(23.0)º70/1(23.0

θθ

⋅−⋅+⋅−⋅≈∆⋅−⋅+⋅+⋅≈∆SWHWSTSWHWST

v

h

)º50/1(59.0)º40/1(12.0)º50/1(59.0)º24/1(12.0

θθθθ

REFERENCES[1] A.Camps et al., “L-Band Sea Surface Emissivity: Preliminary Results of the WISE-2000 Campaign and its Application

to Salinity Retrieval in the SMOS Mission”, Radio Science,Vol. 38, No. 3, 2003.[2] C: Gabarró et al., “A new empirical model of sea surface microwave emissivity for salinity remote sensing”,

Geophysical Research Letters, Vol. 31, L01309, 2004

⊕ SMOS: Soil Moisture and Ocean Salinity is an ESA mission to be launched on 2007. It carries an L-band polarimetric radiometer with multi-angular view capability.⊕ WISE (Wind and Salinity Experiment) field measurements were done to improve sea surface emissivity models at L-band for sea surface salinity (SSS) retrieval: Aradiometer and several buoys were installed in a fixed platform in the NW Mediterranean to measure TB, SST, SSS, WS and SWH.

I. Introduction

C. Gabarró1, J. Font1, A. Camps2, M. Vall-llossera2, M. Portabella31Institut de Ciències del Mar- CSIC. Barcelona. E-mail: cgabarro@icm.csic.es

2 Universitat Politècnica de Catalunya. Dept. Teoria del Senyal iComunicació. Barcelona3 KNMI Koninklijk Nederlands Meteorologisch Instituut. De Bilt. Holland

2

2

2

2

2

2

2

2

2

2mod )()()()()(

SST

ref

SWH

ref

WS

ref

SSS

ref

Tb

BB SSTSSTSWHSWHWSWSSSSSSSTTC

σσσσσ−

+−

+−

+−

+−

= ∑

Wind Speed and Significant Wave Height data:To retrieve these parameters a least squares method is used. TheSSS, WS and SWH values that minimise the distance between measured and modelled TBrepresent the correct value for each parameter.

Looking at the plots of the cost function dependent on SSS, WS and SWH (Figure 1) one can realise that only one minimum is present, so it is possible to retrieve these values without any inconsistency.

The cost functions with restrictions consist on giving a priori knowledge (reference) of the parameters as well as the expected error of these a priori values (σ2 ).

For these calculations HIRLAM and WAM model outputs are used as reference data for WSref and SWHref, respectively. A constant value is used forSSSref and SST is supposed to be known with exact value / an error of 1ºC. Two different values for the standard deviations error of the reference valuesare shown. The errors in retrieved SSS, WS and SWH are:

σ2tb=1, σ2

SSS=0.25, σ2WS=4 and σ2

swh=0.25 σ2tb=1, σ2

SSS=1.0, σ2WS=6.25 and σ2

swh=1.0.|SSS in situ - SSS ret| = 0.20 / 0.22 psu |SSS in situ - SSS ret| = 0.28 / 0.32 psu |WS in situ - WS ret| = 1.03 / 1.07 m/s |WS in situ - WS ret| = 1.11 / 1.08 m/s|SWH in situ - SWH ret| = 0.28/ 0.28 m |SWH in situ - SWH ret| = 0.48/ 0.48 m

Figure 2 compares the retrieved parameter (red), the in situ measurement(green) and the model output (blue) for each parameter for different radiometric measurements. SSS retrieved with this method is better than using directly WS and SWH values from HIRLAM and WAM (|SSS in situ - SSS ret| = 0.34 psu).

II. Objectives⊕ The main objective of this study is to retrieve SSS from WISE measurements (TB) taking the advantage of the multi-angular view.⊕ The possibility of retrieving the auxiliary data from the radiometric measurements themselves has been investigated.

2

2

2

2

2

2

2

2mod )()()()(

SWH

ref

WS

ref

SSS

ref

Tb

BB SWHSWHWSWSSSSSSSTTC

σσσσ−

+−

+−

+−

= ∑Cost function to be minimised to find the correct SSS, WS and SWH values :

The auxiliary data needed to retrieve salinity from rediometer measurements are WS, SWH and SST among others. These data could be obtained from external sources (meteorological and oceanographicmodels, satellite measurements, etc) or from the radiometer measurements themselves. Here the last case is developed.

Figure 1

Figure 2

Figure 3 Figure 4

The errors in retrieved SSS from WISE2001 data using both models and in situ SST, WS and SWH:

WS dep. |SSS in situ - SSS ret| = 0.19 psuWS + SWH dep. |SSS in situ - SSS ret| = 0.13 psu

Sea Surface Salinity: retrieved value in situ measurements

0 5 10 15 20data-set

37

38

39

40

Wind Speed: Retrieved value In situ measurements output of HIRLAM model

0 5 10 15 20data-set

0

5

10

15

Significant Wave Height: Retrieved value in situ measurement ou tput of WAM model

0 5 10 15 20data-set

0

2

4

6

8

COST FUNCTION

• dependent on Wind Speed [1]:

• dependent on WS & SWH [2]: