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 GEDI and ICESat-2, are limited in spatial resolution and photon density, constraining their ability to capture detailed Surface Topography and Vegetation (STV) 3D imagery. Airborne systems, like 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 3D imaging. A novel framework, based on HyperHeight Data Cubes (HHDCs), uses deep learning to transform sparse measurements into 3D images, but its resolution remains constrained by physical limitations of the instruments. This paper 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 finer-scale studies of forest structure and improving applications in forest management and environmental monitoring. The methodologies were tested in three regions of the United States: 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.

Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics—ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI—that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity.

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.

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.

In this paper, experimental results concerning the application of no-reference image quality metrics for automatic quality assessment of reconstructed satellite light detection and ranging (LiDAR) data are discussed. Since satellite LiDARs provide much sparser data compared to airborne systems and their sending to Earth requires additional compression, the received data should be reconstructed. For this purpose, generative neural networks can be applied, including generative diffusion models; however, they require the choice of appropriate parameters, and the reconstruction results should be reliably assessed. Although the physical representation of data is related to the number of received photons, considering the novel data representation based on hyper-height data cubes, it is possible to analyze individual percentiles, representing canopy height models or digital terrain models, as grayscale images. Therefore, their quality may be evaluated using image quality assessment metrics. Due to the lack of reference data, in the application considered, only no-reference metrics may be efficiently applied, even though they have lower correlation with subjective quality perception compared to full-reference metrics. Therefore, the idea of designing combined no-reference metrics optimized for the highest correlation with subjective quality scores is discussed, along with the presentation of the obtained correlation results.