Bayesian fusion of active and passive microwave data for estimating bare soil water content

Evaluates an approach to improve the estimation of soil moisture from remotely sensed data. We focus on two types of sensors: a radiometer and a scatterometer operating at a frequency of 4.6 GHz, which both observe the same portion of the Earth surface. Active and passive microwave systems are sensitive to changes in the dielectric properties of the soil and surface morphological properties. Indeed they show complementary capabilities useful for the quantification of these soil parameters. In this context, a retrieval algorithm based on a Bayesian approach has been developed. Our analysis indicates that an improvement in soil moisture estimation accuracy can be obtained when passive radiometric measurements and active radar data are fused with respect to the estimation from a single source. The evaluated soil moisture values show a reasonable agreement in comparison with in situ measurements.