There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. L-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps (R 2 ∼0.87 instead of ∼0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.