Comment on essd-2025-378

<strong class="journal-contentHeaderColor">Abstract.</strong> Forests are vital for the carbon sequestration, biodiversity conservation, and climate regulation, making the precise and continuous monitoring of forest structure attributes such as canopy height essential. Here we present a two decades long (2004&ndash;2024), 30 m resolution annual canopy height dataset for Italy, developed using a time-series deep learning framework that integrates Landsat optical imagery with LiDAR observations. Two UNET models were independently trained using canopy height reference data from airborne laser scanning (ALS) and NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR mission. Annual canopy height predictions from each model were fused using Bayesian Model Averaging (BMA) to enhance spatial consistency and temporal continuity. Validation against ground-based measurements from the Italian National Forest Inventory (NFI) demonstrated high predictive accuracy (mean absolute error = 3.98 m). To further evaluate the utility of our dataset, we derived a canopy height change-based disturbance product and validated it against observed events (mean precision = 0.64 for 2005&ndash;2016). In addition, we assessed post-disturbance recovery by monitoring canopy height regrowth in areas affected during 2004&ndash;2005, tracking changes annually through 2024 across various Italian biomes. Our results highlight the importance of integrating multi-source remote sensing data with deep learning and Bayesian data fusion for monitoring forest structural dynamics. The final dataset is publicly available via Zenodo and provides a reproducible and scalable resource to support forest research, ecological monitoring, and climate-related policy-making.