Sensing the Earth’s surface topography and vegetation (STV) structure is of critical importance for a myriad of scientific applications. STV metrology relies on lidar, radar, stereophotogrammetry, or a combination of these remote sensing techniques. STV metrology, however, suffers from low spatial and height resolution or sparse coverage if lidars and stereophotogrammetry are deployed at orbital heights. Many scientific applications such as bare Earth, cryosphere, and hydrology, require meter or sub-meter STV observables in spatial resolution with submeter vertical resolution. This work aims to overcome the STV resolution gap by using a simple observation system composed of an orbital Compressive Sensing (CS) lidar aided by high-resolution monocular RGB photography. The system first produces a super-resolved digital surface model by fusing satellite CS lidar photon returns with monocular photography using an image-to-image translation generative Brownian Bridge Diffusion Model. Subsequently, the low photon count lidar measurements together with the high-resolution DSM are then used in a constrained Denoising Diffusion Probabilistic Model to reconstruct super-resolved, wall-to-wall, and feature-rich HyperHeight STV Data Cubes. This approach effectively enhances the resolution for satellite LiDAR imagery while reducing generative model hallucinations, thereby improving the reliability and utility of the resulting data products for Earth studies. The achievable spatial resolution depends on the monocular RGB imagery resolution, the photon density of the training point cloud, and the noise level in the LiDAR sensor.

Spaceborne lidars are essential for monitoring Earth’s ecosystems, particularly in imaging forests, glaciers, and natural hazards. However, current satellite lidar systems, such as NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), are limited in spatial resolution and photon density, constraining their ability to capture detailed surface topography and vegetation (STV) 3-D imagery. Airborne systems, such as NASA’s G-LiHT, offer higher resolution but lack global coverage. To address these limitations, compressive satellite lidars (CS-Lidars) have been recently introduced, utilizing coded laser illumination and dynamic wavelength scanning for wide-field 3-D imaging. A novel framework, based on hyperheight data cubes (HHDCs), uses deep learning to transform sparse measurements into 3-D images, but its resolution remains constrained by the physical limitations of the instruments. This article proposes three approaches using generative diffusion models to achieve super-resolution lidar imaging, enhancing satellite data resolution. These methods involve learning conditional probabilities, guiding models via forward imaging, and leveraging high-resolution side information. The results show substantial improvements in the resolution of satellite lidar data, enabling fine-scale studies of forest structure and improving applications in forest management and environmental monitoring. The methodologies were tested in three regions of USA: Florida, Maryland, and California. The models were trained and tested on the first two, and their zero-shot capabilities were tested on the third, showing comparable results.

Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for LiDAR sensing of Earth. It is based on NASA’s adaptive wavelength scanning LiDAR (AWSL) system. Unlike conventional 1D LiDAR methods, CS-LiDAR utilizes sparse coded laser illumination across a 2D field-of-view. The aim is to compressively capture Earth from hundreds of kilometers above, enabling computational 3D imagery reconstruction with resolution that is comparable to that attained with data collected from just hundreds of meters. The forward imaging model captures the light propagation phenomena affecting the photon pulses transmitted from the sensor to the Earth’s surface and back. This work enhances CS-LiDAR by integrating imaging spectroscopy into a multimodal system and employing a transformer network for the inverse imaging problem, driven by multimodal attention mechanisms. Emulations enabled by enormous observational LiDAR data of Earth, available from NASA’s G-LiHT imaging observatory, highlight the efficacy of methods developed.

This paper proposes an algorithm to enhance the resolution of satellite lidar data using Generative Adversarial Networks (GANs) under the hyperheight data cube framework. A super-resolution algorithm based on adversarial training is applied to overcome the challenges of long-range satellite lidar systems. The algorithm generates high-resolution super-resolved outputs from low-resolution inputs, improving the quality of several lidar representations such as canopy height models and profiles. This approach not only advances lidar-based models but also facilitates sophisticated lidar data analysis for various fields, such as environmental science, urban planning, and disaster management. The super-resolved lidar data provides a more precise depiction of the Earth's surface, opening up new avenues for research and applications in different domains. The framework's effectiveness was validated in the Florida Everglades National Park, where the resolution was increased from a 3m x 6m grid with 10m footprints to a 3m x 3m grid with 3m footprints, and the vertical resolution was enhanced from 0.5m to 0.25m.

Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for LiDAR sensing of Earth. It is based on NASA’s adaptive wavelength scanning LiDAR (AWSL) system. Rather than measuring 1D line footprints over a satellite’s swath path as is the norm today, CS-LiDAR adopts sparse coded laser illumination over a 2D wide field-of-view. The objective is to compressively sense Earth from hundreds of km above Earth to then computationally reconstruct the 3D imagery with resolution and coverage as if the data was collected from just hundreds of meters in height. The forward imaging model captures the light propagation phenomena affecting the photon pulses transmitted from the sensor to the Earth’s surface and back. This paper advances CS-LiDAR on many fronts. First, imaging spectroscopy side-information, often jointly available with LiDARs, is integrated into a multimodal imaging system. Secondly, the inverse imaging problem is cast under a transformer network architecture driven by multimodal attention mechanisms. Finally, by directing the snapshot spectral cameras in front of the LiDAR, the transformer mechanisms autonomously adjust the LiDAR’s beam scanning to focus on specific target locations, thus attaining end-to-end optimal adaptive sampling that can respond to varying observational conditions, surface events, and scientific priorities. Emulations enabled by enormous observational LiDAR data of Earth, available from NASA’s G-LiHT imaging observatory, show the advantages attained by methods developed in this work.