Understory Terrain Estimation Based on the Fusion of Multisource Remote Sensing Data and Machine Learning Models

Accurate understory terrain estimation is a key challenge in ecological modeling and forest resource management. Traditional optical remote sensing is affected by significant signal saturation in vegetated areas, while spaceborne LiDAR systems such as Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) are limited in supporting regional-scale continuous modeling due to their sparse and discrete footprint coverage. This study integrates filtered ICESat-2 understory elevation control points with optical remote sensing data to comprehensively assess the applicability and predictive accuracy of various machine learning models across different regions. By carefully selecting and optimizing models, the most suitable approach for each study area was identified, enabling precise regional-scale understory terrain estimation. Through multisource remote sensing data fusion, a continuous surface elevation model was constructed, substantially enhancing overall estimation accuracy. Experimental results demonstrate a notable accuracy improvement, with mean error (ME) = 0.42 m, root mean square error (RMSE) = 2.80 m, and standard deviation (STD) = 2.77 m. Furthermore, this study systematically quantifies the influence of environmental factors such as forest type, landform features, slope, aspect, and forest canopy height on estimation accuracy. Beyond advancing methodologies for high-precision understory terrain estimation, this study leverages machine learning optimization and multisource data fusion to overcome the limitations of ICESat-2’s footprint coverage, providing robust technical support for global-scale understory terrain monitoring and ecosystem research.