A 3D Spatial–Spectral–Temporal Deep Regression Model for Improving Mangrove Canopy Height Estimation Through Fusion of Optimized Red-Edge Sentinel-2 Bands and Sentinel-1 SAR Data

Mangroves are vital blue carbon ecosystems with high carbon storage, where canopy height is a key parameter for estimating above-ground biomass. This study integrates Sentinel-1 SAR time-series and Sentinel-2 optical imagery and focused on the investigation of Red-Edge (RE) bands for mangrove canopy height estimation. A new RE-based spectral index named REMCH (RE Mangrove Canopy Height) index was developed for improving mangrove canopy height estimation. To improve the estimation results, this study proposed the 3DSST-RECLT model, a 3D spatial–spectral–temporal deep learning regression model that combining ConvLSTM, hybrid 3D–2D convolution, and Swin Transformer. Airborne LiDAR canopy height data served as target data. Results show fusing Sentinel-1 time-series and Sentinel-2 data using the proposed 3DSST-RECLT model achieved satisfactory performance, with the inclusion of RE bands and the REMCH index enhancing the model performance with an average mean absolute error of 1.648 m on the test dataset and outperforming the other models. This study produced mangrove canopy height maps of the coastal zone of South and Southwest Florida for 2017 and 2020 and found an increase in mangrove canopy height between 2017 and 2020. The produced mangrove canopy height map for 2020 was compared with three global canopy height maps, with the map generated in this study exhibiting higher accuracy. This finding indicates the advantage of integrating Sentinel-1 time-series and Sentinel-2 RE bands with a deep learning regression model to improve mangrove canopy height mapping and monitoring.