  Accurate quantification of forest aboveground biomass (AGB) is critical for
understanding carbon accounting in the context of climate change. In this
study, we presented a novel attention-based deep learning approach for forest
AGB estimation, primarily utilizing openly accessible EO data, including: GEDI
LiDAR data, C-band Sentinel-1 SAR data, ALOS-2 PALSAR-2 data, and Sentinel-2
multispectral data. The attention UNet (AU) model achieved markedly higher
accuracy for biomass estimation compared to the conventional RF algorithm.
Specifically, the AU model attained an R2 of 0.66, RMSE of 43.66 Mg ha-1, and
bias of 0.14 Mg ha-1, while RF resulted in lower scores of R2 0.62, RMSE 45.87
Mg ha-1, and bias 1.09 Mg ha-1. However, the superiority of the deep learning
approach was not uniformly observed across all tested models. ResNet101 only
achieved an R2 of 0.50, an RMSE of 52.93 Mg ha-1, and a bias of 0.99 Mg ha-1,
while the UNet reported an R2 of 0.65, an RMSE of 44.28 Mg ha-1, and a
substantial bias of 1.84 Mg ha-1. Moreover, to explore the performance of AU in
the absence of spatial information, fully connected (FC) layers were employed
to eliminate spatial information from the remote sensing data. AU-FC achieved
intermediate R2 of 0.64, RMSE of 44.92 Mgha-1, and bias of -0.56 Mg ha-1,
outperforming RF but underperforming AU model using spatial information. We
also generated 10m forest AGB maps across Guangdong for the year 2019 using AU
and compared it with that produced by RF. The AGB distributions from both
models showed strong agreement with similar mean values; the mean forest AGB
estimated by AU was 102.18 Mg ha-1 while that of RF was 104.84 Mg ha-1.
Additionally, it was observed that the AGB map generated by AU provided
superior spatial information. Overall, this research substantiates the
feasibility of employing deep learning for biomass estimation based on
satellite data.
