A multitemporal algorithm for SMAP data: Overview and preliminary results using experimental data

A multitemporal algorithm (MLTA) to retrieve soil moisture from radar data, originally conceived and validated for the C band radar aboard the Sentinel-1 satellite [1], has been modified/updated in order to ingest data provided by the SMAP mission. The implemented algorithm consists of integrating a dense time series of radar backscatter measurements within a multitemporal inversion scheme based on the Bayesian Maximum A Posteriori (MAP) criterion. Such estimator maximizes the probability density function of the vector of soil parameters (soil moisture and roughness) conditioned to the measurement vector. The calibration and validation tasks have been accomplished by using the data collected during the SMAP Validation Experiment 012 (SMAPVEX012). Preliminary results have assessed the potential of the algorithm at L-band.