Deep Neural Networks for Forest Growing Stock Volume Retrieval: A Comparative Analysis for L-band SAR data

The aim of this study was to evaluate the utility of deep learning (DL) approaches to estimate forest growing stock volume from L-band SAR data over areas characterized by diverse species composition. For comparison, parametric models were also used. When using one independent variable (i.e. HV backscatter coefficient) the lowest estimation errors were observed for the empirical model followed by Random Forests (RF). Increasing the number of independent variables resulted in marginally more accurate results for the machine learning approaches. However, for the studied area, DL approaches did not improve GSV retrieval when compared to RF or empirical modelling suggesting that L-band data sensitivity to GSV values is the main limiting factor.