Abstract
Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.
Highlights
Microwave remote sensing provides a means to quantitatively describe the water content of a shallow near-surface soil layer, wg (Schmugge, 1983)
The analysis of SMOSREX data indicates that local soil moisture observations at depths ranging from 20 cm to 50 cm are significantly correlated to the root-zone soil moisture integrated over the whole profile
Network, it is assumed that scaled soil moisture observations at 30 cm are a good proxy of the scaled root-zone soil moisture
Summary
Microwave remote sensing provides a means to quantitatively describe the water content of a shallow near-surface soil layer, wg (Schmugge, 1983). Estimation of profile soil moisture from intermittent remotely sensed soil moisture data has focused on the assimilation of such data into land surface models (Ragab, 1995; Walker, 2001a; Sabater et al, 2007). Several authors concluded that the Kalman Filter, an optimal sequential assimilation method extensively used in various environmental problems, is well suited for profile soil moisture estimation (Walker et al, 2001b). Other assimilation techniques such as 1DVAR provide good results under controlled conditions (Sabater et al, 2007). In a land surface model, the conversion from surface to root-zone soil moisture may depend on multiple
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