Above-Ground Biomass estimation: a machine learning approach based on multi-angular L-Band passive microwaves brightness temperatures

Passive microwave observations at different frequencies suffer extinction effects of the different vegetation components (branches, leaves, trunk) across the canopy of the soil’s microwave emission. These effects are often represented as a frequency-dependent variable called the Vegetation Optical Depth (VOD), which has been used (recently) to estimate Above-Ground Biomass (AGB). Low frequency observations, more particularly at L-band (1.4 GHz), have been shown to be sensitive to the woody components of plants (and thus to AGB), hence the growing interest in their use to monitor carbon stocks evolution.

In this study, and thanks to the multi-angle capabilities of the SMOS mission, a new approach to estimate AGB maps directly from multi-angular passive L-band Brightness temperatures (TBs) is proposed, thus surpassing the dependence on intermediate variables like the VOD. Biomass estimates are produced from Artificial Neural Networks (ANN), using as reference the three AGB maps of the Climate Change Initiative (CCI) for the years 2010, 2017 and 2018; the SMOS multi-angle TBs for the same years were selected as inputs. The best set of predictors for ANNs and the optimal learning data-set configuration to estimate AGB are proposed based on a sensitivity analysis; the use of TBs in both Vertical and Horizontal polarization, plus a polarization ratio provided the closest biomass estimates to the reference AGB maps.

ANNs trained from a purely data-driven approach explained 76% of AGB variability globally (incidence angles >35º showed high synergies with AGB); a hybrid approach (coupling ANN with variables derived from physically based models) slightly increased this value (+3%). However, when the trained models are applied to datasets from years different than those used during the training stage, a decrease in retrieval’s quality was observed; a new training scheme based on multi-year training sets is presented, results showed more stability from this kind of training schemes for temporal analyses.

Finally, ANN- and VOD-based estimates were compared with respect to different AGB reference maps, the former outperformed the latter in all evaluation metrics. VOD-based inversions tend to underestimate AGB due to their quick saturation (around 200 Mg/ha) on densely forested regions. Additionally, a strong simplification of the spatial variations of AGB was observed; maps produced from this methodology present abrupt transitions between densely and sparsely vegetated areas, a characteristic that was not observed in the reference maps. When using VOD-derived maps these limitations should be considered, especially when employing them to study the temporal evolution of carbon stocks. The ANN methodology here proposed proves to be a promising technique for the estimation of global AGB maps, with robust results both in the spatial representation and in the temporal reproduction of AGB maps.