From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

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.

Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011

“…This soil moisture dataset is based on the statistical blending of daily passive and active satellite microwave observations between November 1978 and December 2010 on a 0.25 • × 0.25 • regular grid. Although satellite sensors only measure moisture in the uppermost few centimeters of the soil profile, these observations generally correlate with moisture variations measured at deeper layers [56]. Soil moisture retrievals fail over very dense canopy and frozen ground while surface water and rough topography distort soil moisture signals and result in poor data quality.…”

Microwave remote sensing of short‐term droughts during crop growing seasons

“…In contrast, the microwave remote sensing of soil moisture not only has global coverage [Bartalis et al, 2007]. Although the sensing depth of satellite data is only a few centimeters, there is generally a close relationship between surface soil moisture and soil moisture in the upper 10 cm [Albergel et al, 2008], which is the depth of the first soil layer for most land surface models. It represents the fastest response of soil moisture dynamics to meteorological anomalies and provides a measure for short-term droughts, especially the flash droughts that usually do not penetrate into deeper soil before their recovery.…”

A physically based approach for the estimation of root-zone soil moisture from surface measurements

“…Several authors have tackled this problem (e.g. Ceballos et al, 2005;De Lange et al, 2008;Albergel et al, 2008). For instance, Ceballos et al (2005) demonstrated that T represents the parameter of the exponential autocorrelation function of soil moisture.…”

“…According to these findings, the authors suggested that the SWI methodology should benefit from a soil texture differentiation. Another attempt to give a physical interpretation of the recession constant (T ) was carried out by Albergel et al (2008), who investigated the correlation of the parameter, T , with soil properties and climate conditions over France. Unfortunately, they did not observe significant relationships between T and the main soil properties (clay and sand fractions, bulk density and organic matter content).…”

“…The root-zone soil moisture can be obtained through the knowledge of the surface soil moisture and a parameter T . The recursive formulation of the method relies on (Albergel et al, 2008) s * 2 t j = s *…”