%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Djamai, N.
%A Magagi, R.
%A Goita, K.
%A Merlin, Olivier
%A Kerr, Yann
%A Walker, A.
%T Disaggregation of SMOS soil moisture over the Canadian prairies
%D 2015
%L fdi:010065473
%G ENG
%J Remote Sensing of Environment
%@ 0034-4257
%K Soil moisture ; SMOS ; Disaggregation ; DISPATCH algorithm ; Canex-SM10 ; Canada
%K CANADA
%M ISI:000364726100022
%P 255-268
%R 10.1016/j.rse.2015.09.013
%U https://www.documentation.ird.fr/hor/fdi:010065473
%> https://www.documentation.ird.fr/intranet/publi/2015/12/010065473.pdf
%V 170
%W Horizon (IRD)
%X In this study, we used the Disaggregation based on Physical And Theoretical scale Change (DISPATCH) algorithm under very wet soil conditions in Western Canada for the disaggregation of coarse resolution 40-km soil moisture derived from the Soil Moisture Ocean Salinity (SMOS) satellite. The algorithm relies on the Soil Evaporative Efficiency (SEE), which was estimated using the 1-km resolution data from the MODerate resolution Imaging Spectoradiometer (MODIS). The study aimed to: (i) evaluate DISPATCH under wet soil conditions, (ii) test the linearity/non-linearity of the relationship between soil moisture and SEE, and (iii) propose a more robust procedure to calibrate the SEE model under very wet soil conditions. The disaggregated soil moisture values were compared to 0-5 cm in situ measurements and the soil moisture derived from the L-MEB (L-band Microwave Emission of the Biosphere) model from airborne brightness temperature at 1.4 GHz collected during the Canadian Experiment for Soil Moisture in 2010 (CanEx-SM10) field campaign. The results show a correlation between 0.7 and 0.8 and bias values similar to 0 m(3)/m(3). The DISPATCH algorithm shows better disaggregation results under very wet soil conditions when a non-linear relationship is considered between SEE and soil moisture instead of a linear model. This is mainly due to the small variability of surface temperature inside the area covered by the SMOS pixel under very wet soil conditions, and the difficulty in accurately estimating the maximum soil temperature (Ts-max), which is a driving factor for SEE. A sensitivity analysis was conducted and it shows that the linear model performs well only if Ts-max can be determined more accurately. The possibility to determine Ts-max using high resolution MODIS data over a larger area than the SMOS pixel is examined and discussed in the paper.
%$ 126 ; 072