Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detection and Ranging (LiDAR) offer accurate height measurements, their application on a global scale is often cost-prohibitive and logistically challenging. In contrast, remote sensing techniques, particularly 3D tomographic reconstruction from Synthetic Aperture Radar (SAR) imagery, provide a scalable solution for global height estimation. SAR images have been used in earth observation contexts due to their ability to work in all weathers, unobscured by clouds. In this study, we use deep learning to estimate forest canopy height directly from 2D Single Look Complex (SLC) images, a derivative of SAR. Our method bypasses traditional tomographic processing, potentially reducing latency from SAR capture to end product. We quantify the impact of varying numbers of SLC images on height estimation accuracy, aiming to inform future satellite operations and optimize data collection strategies. Our results indicate a potential reduction in MAE in increasing the number of SAR inputs from 3 to 7 of 17\%.