1. Estimating SMOS error structure using triple collocation Delphine Leroux, CESBIO, France Yann Kerr, CESBIO, France Philippe Richaume, CESBIO, France 1

2. Soil moisture products at global scale 2 AMSR-E (NSIDC) ERS- ASCAT (TU Wien) Model output (ECMWF) AMSR-E (VUA) TMI (VUA) SSM/I (VUA) Aquarius SMAP Aquarius SMAP How to evaluate SMOS ??? SMOS ? SMOS ?

3. Inter comparison between SMOS soil moisture and … o Ground measurements (point scale) o Other global products (point scale) 3 Statistics -> triple collocation o Global scale ?

4. Structure 1.Triple Collocation method -> Theory and requirements 2.Chosen datasets -> Characteristics and differences 3.Global maps of relative errors -> Maps of errors -> Maps of bias and scale factors 4

5. Triple Collocation – theory (Caires et al., 2003) Starting equation Taking the anomalies 5 Final equation  Maps of the std of the errors  Maps of the bias  Maps of the scale factors 1) Triple CollocationTheoryRequirements r: bias s: scale factor ε: error

6. Triple Collocation - requirements o Strong assumptions :  Mutually independent errors (ε)  No systematic bias between the datasets o Requirements :  100 common dates (Scipal et al., IGARSS 2010) o Results :  Relative errors 6 -> choose properly the 3 datasets -> TC applied to the anomalies and not to the variables directly -> including the 6 closest grid nodes 1) Triple CollocationTheoryRequirements

7. Datasets Frequency (GHz) Incidence angle (°) Instrument resolution (km) Crossing time (A/D) Grid resolution (km) SMOS1.40-55406am / 6pm 15 AMSR-E6.9 – 10.7 - … 5557-6.251:30pm/ 1:30am 25 7 AMSR-E soil moisture derived with the VUA algorithm (Vrije University of Amsterdam) ECMWF product from SMOS Level 2 product (at SMOS resolution and crossing time) 2) DatasetsChosen datasetsNumber of triplets

8. Number of triplets for 2010 8 2) DatasetsChosen datasetsNumber of triplets Difficulties for regions with mountains, forests, wetlands, …

9. Std of SMOS errors 9 3) Global maps of …relative errorsbiasscaling factors Good results in North America, North Africa, Middle East, Australia. Land contamination in Asia (Richaume et al., RAQRS, 2010).

10. Std of AMSR-E(VUA) errors 10 3) Global maps of …relative errorsbiasscaling factors Good results in the same areas as SMOS.

11. Std of ECMWF errors 11 3) Global maps of …relative errorsbiasscaling factors

12. Comparison over continents 12 3) Global maps of …relative errorsbiasscaling factors RELATIVE ERRORS ! ! SMOS is often between or close to the two values except in Asia

13. 13 3) Global maps of …relative errorsbiasscaling factors Bias : AMSR-E(VUA) - SMOS Very high bias for high latitudes (mainly due to the vegetation) Mean bias around 0.1

14. 14 3) Global maps of …relative errorsbiasscaling factors Bias : ECMWF - SMOS High bias for high latitudes but more homogeneous Mean bias around 0.2-0.3

15. Scale factor AMSR-E(VUA) 15 3) Global maps of …relative errorsbiasscaling factors Scale >1 higher dynamic than SMOS Scale <1 lower dynamic than SMOS

16. Scale factor ECMWF 16 3) Global maps of …relative errorsbiasscaling factors Unlike the bias maps, there is no obvious structure for the scale factor

17. Conclusions o As part of the validation process, triple collocation compares 3 different datasets at a global scale : SMOS, AMSR-E/VUA and ECMWF o SMOS and AMSR-E/VUA have the same performance areas, but ECMWF and VUA give the best results o SMOS algorithm is still improving and it can be considered as a good start o Further work : apply triple collocation to other triplets (SMOS-NSIDC-ASCAT, etc…) and apply it with 2011 data 17

18. Thank you for your attention 18 Any questions ?