Analysis of different synergy schemes to improve SMOS soil moisture accuracy or spatial resolution

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Analysis of different synergy schemes to improve SMOS soil moisture accuracy or spatial resolution

Andre Chanzy, B. Berthelot, Sylvain Cros, M. Weiss, Jean-Pierre Wigneron, J.-C. Calvet, Thierry Pellarin

To cite this version:

Andre Chanzy, B. Berthelot, Sylvain Cros, M. Weiss, Jean-Pierre Wigneron, et al.. Analysis of different synergy schemes to improve SMOS soil moisture accuracy or spatial resolution. 6. SMOS Workshop, May 2006, Copenhaguen, Denmark. 1 p. �hal-02819064�

Analysis of different synergy schemes to improve SMOS soil moisture accuracy or spatial resolution

A. Chanzy, B. Berthelot, S. Cros, M. Weiss, J.-P. Wigneron, J.-C Calvet and T. Pellarin

The soil moisture retrieval algorithm is based on the inversion of semi-empirical parameterization for soil (Choudhury model) and vegetation (thau-omega emission model) combined to the soil emission. One of the major problems in the model inversion is the heterogeneity within the pixel and the crude spatial resolution allowed by SMOS. The heterogeneity is partially taken into account by considering four classes: permanent bare soil, forest, low vegetation and open water. In addition, snow and frozen soil lead to specific models. From SMOS angular and polarimetric measurement and knowing the land class and the occurrence of frozen soil or snow, it is possible to retrieve soil moisture with a reasonable success rate (pixels leading to accuracy better than 0.04 m3/m3). However, additional layers of information can be added to improve the retrieval. The aim of the presentation is to analyze different synergy schemes. The work was implemented over the world using the synthetic data base simulated at CNRM (Pellarin et al. 2003) and a data base, also simulated by the CNRM, over one SMOS pixel in which spatial heterogeneity was taken into account at the kilometric resolution. Following schemes were analyzed :

- Use of optical products that lead to estimate the actual bare fraction thanks to fcover products. This can be used to update the bare soil fraction which is the resultant of the permanent bare soil and the low vegetation fraction left bare at the time of smos acquisition. Results have shown strong improvement when the actual soil fraction is taken into account

- The use of temperature observations (surface temperature, air temperature) to constrain the effective temperature in the retrieval algorithm.

- The combined use of thermal infrared data and NDVI to dis-aggregate the SMOS data. The trapezoid approach was used to compute a water stress index which was used to scale moisture variations within the SMOS pixel

- The use of SAR to disaggregate SMOS pixels.

Results are discussed. Improvements were evaluated against results given by the retrieval algorithm which is the closest to that of soil moisture product in SMOS ATBD. The evaluation was done on the RMSE and the number of pixels having a RMSE better than 0.04 m3/m3. The RMSE is computed from daily soil moisture retrieval made through the year. For the dis-aggregation, improvement is quantified for the subpixels against an average soil moisture value.

Pellarin et al, 2003, Two year global simulation of L-band brightness temperatures over land ,

IEEE Trans. Geosc. Remote Sens., 41(9), 2135-2139.