  The integration of multisource remote sensing data and deep learning models
offers new possibilities for accurately mapping high spatial resolution forest
height. We found that GEDI relative heights (RH) metrics exhibited strong
correlation with the mean of the top 10 highest trees (dominant height)
measured in situ at the corresponding footprint locations. Consequently, we
proposed a novel deep learning framework termed the multi-modal attention
remote sensing network (MARSNet) to estimate forest dominant height by
extrapolating dominant height derived from GEDI, using Setinel-1 data, ALOS-2
PALSAR-2 data, Sentinel-2 optical data and ancillary data. MARSNet comprises
separate encoders for each remote sensing data modality to extract multi-scale
features, and a shared decoder to fuse the features and estimate height. Using
individual encoders for each remote sensing imagery avoids interference across
modalities and extracts distinct representations. To focus on the efficacious
information from each dataset, we reduced the prevalent spatial and band
redundancies in each remote sensing data by incorporating the extended spatial
and band reconstruction convolution modules in the encoders. MARSNet achieved
commendable performance in estimating dominant height, with an R2 of 0.62 and
RMSE of 2.82 m, outperforming the widely used random forest approach which
attained an R2 of 0.55 and RMSE of 3.05 m. Finally, we applied the trained
MARSNet model to generate wall-to-wall maps at 10 m resolution for Jilin,
China. Through independent validation using field measurements, MARSNet
demonstrated an R2 of 0.58 and RMSE of 3.76 m, compared to 0.41 and 4.37 m for
the random forest baseline. Our research demonstrates the effectiveness of a
multimodal deep learning approach fusing GEDI with SAR and passive optical
imagery for enhancing the accuracy of high resolution dominant height
estimation.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing