%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Leroux, D. J.
%A Kerr, Yann
%A Wood, E. F.
%A Sahoo, A. K.
%A Bindlish, R.
%A Jackson, T. J.
%T An approach to constructing a homogeneous time series of soil moisture using SMOS
%D 2014
%L PAR00011388
%G ENG
%J IEEE Transactions on Geoscience and Remote Sensing
%@ 0196-2892
%K Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) ; cumulative density function (CDF) matching ; copulas ; Soil Moisture and Ocean Salinity (SMOS) ; soil moisture ; time series
%K ETATS UNIS
%M ISI:000328939500001
%N 1
%P 393-405
%R 10.1109/tgrs.2013.2240691
%U https://www.documentation.ird.fr/hor/PAR00011388
%V 52
%W Horizon (IRD)
%X Overlapping soil moisture time series derived from two satellite microwave radiometers (the Soil Moisture and Ocean Salinity (SMOS) and the Advanced Microwave Scanning Radiometer-Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies for generating long homogeneous time series of soil moisture are considered. Generated soil moisture time series using only morning satellite overpasses are compared to ground measurements from four watersheds in the U. S. with different climatologies. The two methods, cumulative density function (CDF) matching and copulas, are based on the same statistical theory, but the first makes the assumption that the two data sets are ordered the same way, which is not needed by the second. Both methods are calibrated in 2010, and the calibrated parameters are applied to the soil moisture data from 2003 to 2009. Results from these two methods compare well with ground measurements. However, CDF matching improves the correlation, whereas copulas improve the root-mean-square error.
%$ 126 ; 020