In this work, a multitemporal algorithm (MLTA), originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated in order to retrieve soil moisture from L-Band radar data, such as those provided by the NASA Soil Moisture Active Passive (SMAP) mission. Such type of algorithm may deliver frequent and more accurate soil moisture maps mitigating the effect of roughness and vegetation changes, which are assumed to occur at longer temporal scales with respect to the soil moisture changes. Within the multitemporal inversion scheme based on the Bayesian Maximum A Priori (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model which includes the contribution from vegetation. The calibration and validation tasks have been accomplished by using the data collected during the SMAP Validation Experiment 12.The SMAPVEX12 campaign consists of L-Band images collected by the UAVSAR sensor, in situ soil moisture data and measurements of vegetation parameters, collected during the growing season of several crops (pasture, wheat, soybean, corn, etc.). They have been used to update the forward model for bare soil scattering at L-band with respect to the Oh and Sarabandi model previously used at C band. Moreover, the SMAPVEX12 data have been also used to tune a simple vegetation scattering model which considers two different classes of vegetation: those producing mainly single scattering effects, and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction.