High altitude computational lidar emulation and machine learning reconstruction for Earth sciences

Lidar remote sensing systems are utilized across different platforms such as satellites, airplanes, and drones. These platforms play a crucial role in determining the sampling characteristics of the imaging system they carry. For instance, low-altitude lidars offer high photon count and spatial resolution but are limited to small, localized areas. In contrast, satellite lidars cover larger areas globally but suffer from lower photon counts and sparse sampling along swath line trajectories. This paper presents current state-of-the-art approaches in addressing the limitations of satellite imaging systems using a novel class of satellite remote sensing lidars coined Compressive Satellite Lidars (CS-Lidars). CS-Lidars leverage compressive sensing and machine learning techniques to capture Earth's features from hundreds of kilometers above its surface. By doing so, they reconstruct 3D imagery with high resolution and coverage, akin to data collected from airborne platforms flying hundreds of meters above ground level. The paper also compares different machine learning methods used to reconstruct compressive lidar measurements, aiming for high-resolution, dense coverage, and broad field-of-view per swath pass. Training data for these machine learning models is obtained from NASA's G-LiHT imaging missions.