A Bayesian Approach for an Aquarius Soil Moisture Product

CINTIA BRUSCANTINI; FRANCISCO GRINGS; MATIAS BARBER; PABLO PERNA; HAYDEE KARSZENBAUM

Buenos Aires

Conferencia; Aquarius/SAC-D Science Meeting; 2013

NASA, CONAE

Several retrieval algorithms were developed to retrieve soil moisture (SM) from passive remote sensing data. The most commonly used are the Single Channel Algorithm (SCA), the Dual Channel Algorithm (DCA) and LPRM. In this paper, a novel retrieval algorithm (BRA, Bayesian Retrieval Algorithm) is developed, which uses Bayesian inference to retrieve SM and optical depth from both H & V channels. Bayesian likelihood is derived in a non parametric manner, in such a way to be a function of ancillary parameters uncertainties (uncertainties in the parameters needed for the retrieval). As a major advantage, prior knowledge for SM and optical depth can be directly included as inputs to BRA and may improve the retrieval. The advantages of BRA compared to previously mentioned retrievals are: i) errors on the retrieved variables can be estimated in an univocal way, ii) it gives the possibility to use prior information about the retrieved variables (provided by other sensors or in situ historical data), iii) it can handle uncertainties on the ancillary parameters. The comparison of the retrieval performance of the different algorithms was carried out using an Observing System Simulation Experiment (OSSE) developed for the Aquarius/SAC-D L-band radiometer (Bruscantini et al., 2013).