Integrating Sentinel-1&2 and Icesat-2 Data for Forest Canopy Height Estimation and Mapping

This study was conducted to reduce the bias associated with using ICESat-2 data in canopy height estimation and mapping by integrating the features of Sentinel-1&2. The parameters derived from Sentinel-1 and Sentinel-2 images were used for the canopy height modeling through the gradient boosting regression model. The ICESat-2-based canopy height served as the training data while the ALS data was used for validating the accuracy of the predicted canopy height product. The result showed a low correlation between the ICESat-2 and ALS-based canopy height. Nevertheless, the accuracy of the predicted canopy height was increased, from 14.08m to 13.89m based on RMSE and from 55.62% to 54.86% based on PRMSE, when the features of Sentinel-1 was integrated with the features of Sentinel-2. Further, low canopy height values tend to be overpredicted while high canopy height tend to be underpredicted. Thus, canopy height correction models can be developed to augment issues of low accuracy associated with canopy height modeling using ICESat-2.