A time series based method for estimating relative soil moisture with ERS wind scatterometer data

[1] The radar backscattering coefficient is mainly determined by surface soil moisture, vegetation and land surface roughness under a given configuration of the satellite sensor. It is observed that the temporal variations of the three variables are different, the variation of vegetation and roughness are at the longer temporal scales corresponding to climate and cultivation practices, while soil moisture varies at a shorter temporal scale in response to weather forcing. Based on this hypothesis, a robust method for the determination of relative soil moisture is proposed for application to the ERS Wind Scatterometer database and the Pathfinder AVHRR dataset. Ground measured soil moisture data collected during GAME/Tibet field experiment are used to validate the proposed method. The results show that the estimated relative soil moisture corresponds closely to local precipitation at two GAME/Tibet field sites. The volumetric soil moisture converted from the Wind Scatterometer estimated relative soil moisture is in good agreement with the measured 0-4 cm topsoil volumetric soil moisture. The regional distributions of relative soil moisture are evaluated and correlated well to the ground measured volumetric soil moisture. It is concluded that the large footprint Wind Scatterometer dataset can capture ground soil wetness variation in arid and semi-arid zone with the aid of vegetation information derived from other sensors.