Abstract
This study aimed at evaluating the potential of machine learning (ML) for estimating forest
biomass from polarimetric Synthetic Aperture Radar (SAR) data. Retrieval algorithms based on two
dierent machine-learning methods, namely Artificial Neural Networks (ANNs) and Supported
Vector Regressions (SVRs), were implemented and validated using the airborne polarimetric SAR
data derived from the AfriSAR, BioSAR, and TropiSAR campaigns. These datasets, composed
of polarimetric airborne SAR data at P-band and corresponding biomass values from in situ and
LiDAR measurements, were made available by the European Space Agency (ESA) in the framework
of the Biomass Retrieval Algorithm Inter-Comparison Exercise (BRIX). The sensitivity of the SAR
measurements at all polarizations to the target biomass was evaluated on the entire set of data from
all the campaigns, and separately on the dataset of each campaign. Based on the results of the
sensitivity analysis, the retrieval was attempted by implementing general algorithms, using the entire
dataset, and specific algorithms, using data of each campaign. Algorithm inputs are the SAR data
and the corresponding local incidence angles, and output is the estimated biomass. To allow the
comparison, both ANN and SVR were trained using the same subset of data, composed of 50% of the
available dataset, and validated on the remaining part of the dataset. The validation of the algorithms
demonstrated that both machine-learning methods were able to estimate the forest biomass with
comparable accuracies. In detail, the validation of the general ANN algorithm resulted in a correlation
coecient R = 0.88, RMSE = 60 t/ha, and negligible BIAS, while the specific ANN for data obtained R
from 0.78 to 0.94 and RMSE between 15 and 50 t/ha, depending on the dataset. Similarly, the general
SVR was able to estimate the target parameter with R = 0.84, RMSE = 69 t/ha, and BIAS negligible,
while the specific algorithms obtained 0.22 <= R <= 0.92 and 19 <= RMSE <= 70 (t/ha). The study also
pointed out that the computational cost is similar for both methods. In this respect, the training is
the only time-demanding part, while applying the trained algorithm to the validation set or to any
other dataset occurs in near real time. As a final step of the study, the ANN and SVR algorithms were
applied to the available SAR images for obtaining biomass maps from the available SAR images.