Soil moisture estimation over vegetated terrains using multitemporal remote sensing data

Abstract

A new method for retrieving soil moisture content over vegetated fields, employing multitemporal radar and optical images, is presented. It is based on the integration of the temporal series of radar data within an inversion scheme and on the correction of the vegetation effects. The retrieval algorithm uses the Bayesian maximum posterior probability and assumes the existence of a relation among the soil conditions at the different times of the series. The correction of the vegetation effects models the variation, with respect to the initial time of the series, of the component of the backscattering coefficient due to the soil characteristics as function of the variations of the measured backscattering coefficient and of the biomass. The method is tested on the data acquired throughout the SMEX02 experiment. The results show that measured and estimated soil moistures are fairly well correlated and that the performances of multitemporal retrieval algorithm are better than those obtained by employing one radar acquisition, especially in terms of capability to detect soil moisture changes. Although the approach to correct the vegetation effects on radar observations needs to be further assessed on different sets of data, this finding demonstrates that the proposed method has a potential to improve the quality of the soil moisture retrievals.