Estimating SMOS error structure using triple collocation

1 Delphine Leroux, CESBIO, France Yann Kerr, CESBIO, France Philippe Richaume, CESBIO, France

Soil moisture products at global scale AMSR-E (VUA) SSM/I (VUA) How to evaluate SMOS ???

2 TMI (VUA) Aquarius SMAP SMOS ?

ERS ASCAT (TU Wien) AMSR-E (NSIDC) Model output (ECMWF)

Inter comparison between SMOS soil moisture and … o Ground measurements (point scale) 3 o Other global products (point scale) o Global scale ?

Statistics -> triple collocation

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

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

1) Triple Collocation Theory Requirements

Triple Collocation - requirements

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

2) Datasets

Datasets

Chosen datasets Number of triplets SMOS AMSR-E

Frequency (GHz) Incidence angle (°)

1.4

6.9 – 10.7 … 0-55 55

Instrument resolution (km) Crossing time (A/D) Grid resolution (km)

40 57-6.25

6am / 6pm 1:30pm/ 1:30am 15 25 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) 7

2) Datasets Chosen datasets Number of triplets

Number of triplets for 2010

8 Difficulties for regions with mountains, forests, wetlands, …

3) Global maps of … relative errors

Std of SMOS errors

bias scaling factors 9 Good results in North America, North Africa, Middle East, Australia.

Land contamination in Asia (Richaume et al., RAQRS, 2010).

3) Global maps of … relative errors bias scaling factors

Std of AMSR-E(VUA) errors

10 Good results in the same areas as SMOS.

3) Global maps of … relative errors

Std of ECMWF errors

bias scaling factors 11

3) Global maps of … relative errors bias scaling factors

Comparison over continents

12 !

RELATIVE ERRORS SMOS is often between or close to the two values except in Asia

3) Global maps of … relative errors bias scaling factors

Bias : AMSR-E(VUA) - SMOS

13 Very high bias for high latitudes (mainly due to the vegetation) Mean bias around 0.1

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

3) Global maps of … relative errors bias

Scale factor AMSR-E(VUA)

scaling factors 15 Scale >1 higher dynamic than SMOS Scale <1 lower dynamic than SMOS

3) Global maps of … relative errors

Scale factor ECMWF

bias scaling factors 16 Unlike the bias maps, there is no obvious structure for the scale factor

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

Thank you for your attention

Any questions ?

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