A Novel Canopy Height Mapping Method Based on UNet++ Deep Neural Network and GEDI, Sentinel-1, Sentinel-2 Data

As a vital carbon reservoir in terrestrial ecosystems, forest canopy height plays a pivotal role in determining the precision of biomass estimation and carbon storage calculations. Acquiring an accurate Canopy Height Map (CHM) is crucial for building carbon budget models at regional and global scales. A novel UNet++ deep-learning model was constructed using Sentinel-1 and Sentinel-2 multispectral remote sensing images to estimate forest canopy height data based on full-waveform LiDAR measurements from the Global Ecosystem Dynamics Investigation (GEDI) satellite. A 10 m resolution CHM was generated for Chaling County, China. The model was evaluated using independent validation samples, achieving an R2 of 0.58 and a Root Mean Square Error (RMSE) of 3.38 m. The relationships between multiple Relative Height (RH) metrics and field validation data are examined. It was found that RH98 showed the strongest correlation, with an R2 of 0.56 and RMSE of 5.83 m. Six different preprocessing algorithms for GEDI data were evaluated, and the results demonstrated that RH98 processed using the ‘a1’ algorithm achieved the best agreement with the validation data, yielding an R2 of 0.55 and RMSE of 5.54 m. The impacts of vegetation coverage, assessed through Normalized Difference Vegetation Index (NDVI), and terrain slope on inversion accuracy are explored. The highest accuracy was observed in areas where NDVI ranged from 0.25 to 0.50 (R2 = 0.77, RMSE = 2.27 m) and in regions with slopes between 0° and 10° (R2 = 0.61, RMSE = 2.99 m). These results highlight that the selection of GEDI data preprocessing methods, RH metrics, vegetation density, and terrain characteristics (slope) all have significant impacts on the accuracy of canopy height estimation.