Future satellite LiDARs such as NASA's CASALS promise global monitoring but suffer from coarse footprints, sparse sampling, and severe photon/background/readout noise. We present a unified Bayesian framework that jointly performs compressed-sensing (CS) recovery, denoising, and super-resolution of HyperHeight Data Cubes (HHDCs). The inverse problem combines a physics-informed hierarchical likelihood that models PSF blur/decimation, photon statistics with background, and readout noise, with a data-driven prior parameterized by a diffusion model. Posterior-guided diffusion sampling couples the prior score with the likelihood gradient, yielding high-posterior reconstructions without hand-tuned regularizers. To our knowledge, this is the first evaluation on real CASALS engineering-flight data (Virginia, Nov. 2024) in addition to extensive emulation. On emulated datasets, the method improves PSNR by up to 5 dB over PnP-ADMM+BM3D and strong supervised deep-learning baselines (3D U-Net, SwinIR) while achieving better SSIM/LPIPS, especially at low SNRs and 25-50% illumination. On real data, it improves no-reference IQA (e.g., CHM NIQE 5.45 vs. 8.06) and achieves 0.085 bits/voxel better log-likelihood under the hierarchical model, with similar gains under a Gaussian approximation. The approach narrows the resolution-noise gap between satellite and airborne LiDAR, enabling more reliable canopy and terrain products for biomass, forest structure, and disaster applications.

This work reports a unified liquid-crystal-integrated silicon nitride (LC-SiN) phase-tuning platform that operates efficiently at both near-visible (780 nm) and telecom (1550 nm) wavelengths, enabling low-loss, low-power, and highly efficient phase control for integrated photonics. At 780 nm, lithographically defined LC-filled trenches above SiN microring resonators with lateral electrodes provide strong overlap between the guided mode and a high-birefringence nematic LC, yielding continuous and reversible resonance tuning approaching one full free spectral range, corresponding to approximately 2 pi phase modulation with a record-low V pi center dot L of 0.014 V center dot cm and static power consumption on the order of nanowatts. Time-resolved measurements reveal millisecond-scale electrically driven switching and multi-second relaxation dynamics, enabling quasi-static retention of the programmed optical state without continuous power, which is attractive for dense, reconfigurable circuits where configuration updates are infrequent. Extending this architecture to a multi-level SiN photonic platform at 1550 nm, the same LC-SiN integration scheme supports compact, voltage-controlled phase shifters and variable-tap amplitude modulators suitable for long delay lines, star couplers, and tunable couplers in complex dispersive circuits such as serial arrayed waveguide gratings. The combination of ultra-low-loss SiN waveguides and LC-based phase tuning addresses key limitations of conventional thermo-optic and carrier-based devices by simultaneously improving phase efficiency, reducing static power, and maintaining compatibility with both near-visible and telecom bands. Together, these results establish LC-SiN integration as a versatile, wavelength-agnostic platform for energy-efficient phase control in advanced photonic systems, including reconfigurable filters, programmable photonic signal processors, chip-scale LiDAR, and quantum and sensing architectures

This work presents an overview of a hyperspectral microwave-photonic spectrometer and demonstrates the system end-to-end noise equivalent delta temperature (NEDT) performance. The system aims at augmenting the remote sensing capability from space, with a focus on the Earth's planetary boundary layer (PBL) thermal microwave (MW) spectral region (0-200 GHz). Combining a Photonic Integrated Circuit (PIC) channelizer and an application-specific integrated circuit (ASIC) spectrometer, the PIC & ASIC (PICASIC) module can process 40 GHz spectra at hyperspectral (4 MHz) resolution. The photonic technology is agnostic to the spectral region, and multiple photonic modules can cover the entire 200 GHz spectrum. Measured results of the end-to-end system NEDT agree with predicted values, confirming that the NEDT is primarily dominated by the noise figure of the MW front-end, with the optical link adding no significant noise. The data also demonstrate that a single module enables simultaneous super-spectral (4 GHz) and hyperspectral (4 MHz) resolution channel analysis across a 40 GHz range.

This work demonstrates the noise performance of the photonic back end of a hyperspectral microwave-photonic spectrometer aimed at augmenting the remote sensing capability from space, with a focus on the Earth’s planetary boundary layer (PBL). Using a distributed-feedback (DFB) laser and an electro-optical phase modulator, a 40 GHz spectrum is upconverted into the optical domain and sent to a 10-channel photonic integrated serial array waveguide grating (SAWG) where the spectra are separated into 4 GHz channels. Spectral content of each channel is downconverted to baseband using a second DFB laser and measured using an electrical spectrum analyzer (ESA) at a resolution of 8 MHz. Measured results of the end-to-end system Noise Figure (NF) for all channels agree with the theoretical analysis and demonstrate the suitability of microwave photonics for atmospheric studies.

The Advanced Ultra-high Resolution Optical Radiometer (AURORA) Pathfinder is a 2024 directed award funded by the NASA Earth Science and Technology Office (ESTO) and led by NASA Goddard Space Flight Center in collaboration with academia and private industry partners. AURORA Pathfinder aims to demonstrate the combined use of Photonic Integrated Circuit (PIC) and Application-Specific Integrated Circuit (ASIC) technologies in space. It is the first steppingstone towards enabling broad-band, contiguous and hyperspectral measurements of the full Earth's thermal microwave radiation spectrum from space. This paper presents the instrument design, the spectral characteristics and expected measurement sensitivity. It concludes highlighting the potential synergies between the NASA Planetary Boundary Layer Decadal Survey and the NOAA next generation hyperspectral microwave programs.

Satellite LiDAR systems are challenged by limited spatial resolution, photon scarcity, and noise, often yielding coarse and noisy reconstructions of intricate 3D structures such as forest canopies and terrain.1 This work presents a novel framework that integrates generative adversarial networks (GANs)2, 3 with a fixed, Optimized illumination pattern to improve the efficiency and quality of satellite compressive LiDAR data acquisition and reconstruction. Using a single illumination pattern-designed with binary4 and M-ary5 sampling and generated through GAN-based learning-our approach efficiently captures critical data across all regions, preserving key structural features while reducing redundancy in data collection. This method addresses the inverse problem of reconstructing high-resolution images from photon-limited measurements, optimizing photon sampling rates and laser shot locations universally, regardless of terrain type or vegetation density. Incorporating the GAN with the generated illumination pattern, we transform low-resolution inputs into end-to-end optimized high-fidelity outputs. We validate the approach using NASA's CASALS system6, 7 and canopy height mapping at the Smithsonian Environmental Research Center, demonstrating robust reconstruction fidelity comparable to Blue Noise sampling.5 By enhancing data acquisition efficiency and minimizing processing demands with a single, scalable illumination pattern, this framework offers significant promise for environmental monitoring, resource management, and future Earth observation missions.

Satellite LiDAR systems are inherently constrained by spatial resolution and photon density due to the vast distances involved, resulting in coarse spatial footprints and noisy measurements that obscure fine-scale 3D structures. In this work, we present a novel diffusion-based framework for jointly denoising and performing super-resolution on these photon-limited LiDAR data. Our method is grounded in a Bayesian formulation that balances a physics-inspired likelihood—capturing key processes such as Gaussian beam convolution, multinomial photon-count statistics, and sensor noise—with a powerful learned prior encoded by diffusion models. By coupling these components, the approach iteratively refines low-resolution, noisy measurements into high-fidelity reconstructions of forest canopies and terrain. We apply the proposed technique to canopy height and terrain mapping at the Smithsonian Environmental Research Center, demonstrating robust performance even under severe noise conditions where signal-to-noise ratios drop below -1dB. Extensive experiments show negligible losses in both structural similarity and peak signal-to-noise ratio above moderate noise levels, while still maintaining strong reconstruction fidelity at extreme photon and noise limitations.

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.

Sensing the Earth's surface topography and vegetation (STV) structure is of critical importance for a myriad of scientific applications. STV metrology relies on light detection and ranging (LiDAR), radio detection and ranging (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 submeter 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 (DSM) by fusing satellite CS LiDAR photon returns with monocular photography using an image-to-image (I2I) translation generative Brownian bridge diffusion model (BBDM). Subsequently, the low photon count LiDAR measurements together with the high-resolution DSM are then used in a constrained denoising diffusion probabilistic model (DDPM) 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.

We report development progress of a Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) for topography swath mapping from space. The beam scanning was demonstrated by fast wavelength tuning and grating dispersion, and near quantum limited performance was measured at 1550 nm. A 1040 nm CASALS prototype is being developed for Earth science. The laser is rapidly tuned across 13 nm and carved into 2-ns pulses to scan 256 tracks. At the grating-spectrometer-based receiver, returns from each track are filtered spatially and spectrally and imaged onto a HgCdTe APD-array. The detected signals are time-division-multiplexed to only two high-speed analog-to-digital converters and range-gated to reduce data volume. The design can be adapted for gapless sub-meter resolution lunar swath mapping at 1550 nm. 3D imaging of landing site with 4 M footprints per second is enabled by inserting a 4-Hz 2D steering mirror. The lidar can also perform navigation measurements up to 100 km.

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.

We present the design and performance of a Concurrent Artificially-intelligent Spectrometry and Adaptive Lidar System (CASALS) for 3D imaging from Space. With a single fast wavelength tuning laser, CASALS accomplishes a 1,200 resolvable spots swath mapping by grating dispersion wavelength steering. Any subset of these 1,200 spots can be selected by wavelength switching. The validation operating principle was accomplished and reported in IGASS-2022. With configurable base design, we report the designs and progress of the CASALS airplane campaign with 256 contiguous ground spots. It is accomplished with a single fast tuning lase at 1040-nm, 1.152MHz tuning rate, and pulse modulated 2-ns on each wavelength. Return pulses are mapped to an eight-pixel detector array with single-photon sensitivity. The lidar returns are time-multiplexed to two outputs that are digitized with two 1-GSPS-digitizer. A grating spectrometer rejects solar background noise spatially and spectrally. We are developing a 1040-nm CASALS intended for multiple LEO orbit missions: Earth Venture Mission on ESPA Grande, STV Mission on ESPA Grande SmallSat, STV Mission on spacecraft equivalent to ICESat-2.

Surface Topography and Vegetation (STV) is a NASA targeted observable for maturation into an observing system architecture. STV will acquire high-resolution, global height measurements, including bare surface land topography, ice topography, vegetation structure, and shallow water bathymetry. These measurements serve a broad range of science and applications objectives that span solid earth, cryosphere, biosphere and hydrosphere disciplines. A common set of measurements could meet many of the community needs. STV objectives would be best met by new observing strategies that employ flexible multi-source and sensor measurements from a variety of orbital and sub-orbital assets. Science and application objectives would be best met by new, 3-dimensional observations from lidar, radar, and stereoimaging. Simulations, experiments, data analysis and technology development in interferometric SAR, lidar and stereo photogrammetry approaches, platform options and system architectures will all mature STV toward an observing system.

Compressive satellite LiDAR (CS-LiDAR) has been recently introduced as a radically different computational sensing and reconstruction approach for light detection and ranging (LiDAR) sensing of Earth. It is based on NASA's adaptive wavelength scanning LiDAR (AWSL) system. Rather than measuring 1-D line footprints over a satellite's swath path as is the norm today, CS-LiDAR adopts sparse coded laser illumination over a 2-D wide field-of-view. The objective is to compressively sense Earth from hundreds of kilometers above Earth to then computationally reconstruct the 3-D imagery with resolution and coverage as if the data were 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 article advances CS-LiDAR on many fronts. First, imaging spectroscopy side information, often jointly available with LiDARs, is integrated into a multimodal imaging system. Second, 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 Goddard Lidar, Hyperspectral, Thermal (G-LiHT) imaging observatory, show the advantages attained by methods developed in this work.

An ultra-compact, narrow-bandwidth, and high-density photonic integrated channelizer has been developed on a silicon nitride platform and demonstrated for parallel processing of wide band hyperspectral microwave spectra. This device is based on an updated generation of arrayed waveguide gratings (AWG) named serial-AWG (SAWG). The design consists of 33 tunable optical delay lines and 10 output channels. Fabrication inaccuracies are compensated by the use of metal heaters to obtain narrow bandwidth and channel separation, and high side lobe suppression. Our experimental results demonstrate ten 2.6 GHz-bandwidth channels, separated by 3.9 GHz and with 20 dB of side-lobes suppression.

Low-altitude airborne lidars deliver high spatial resolution swath mapping using dense laser footprint sampling but only in limited areas, while satellite lidars offer global sampling but are hampered by low resolution due to sparse footprints. This work presents a novel approach to satellite lidar remote sensing designed to address the low spatial resolution by leveraging the principles of compressive sensing and machine learning applied to a highly efficient, adaptive lidar capable of dense footprint sampling. Compressive sensing enables the distribution of footprints across a swath with a density appropriate to recover the features of interest, without unnecessarily oversampling the terrain. Machine learning techniques are employed to reconstruct the compressive lidar measurements, leading to high-resolution, dense coverage, and a broad field-of-view per swath pass. HyperHeight Data Cubes are introduced, which offer a wealth of information about the 3D structure of a scene, including digital surface models, canopy height models and the internal organization of canopies. Training data was obtained from NASA's G-LiHT airborne lidar, and simulations of satellite observations performed on various forest types across the US demonstrate the efficacy of the new lidar imaging approach.

Performance of space-based optics could be greatly enhanced by using deployable origami-based arrays, which can offer a large aperture size relative to their stowed volume when compared to traditional technology, thus improving imaging quality. In this work, we select, develop, and adapt the origami flasher pattern to serve as the foundation for a deployable array that shows promise for meeting stringent optical requirements. We apply a novel thickness accommodation technique, outline an approach for implementing the technique, improve stability by adjusting geometric characteristics of the pattern, and create an array of frames for housing optical elements in a co-planar configuration. Problems of non-rigid foldability in the flasher pattern are addressed. A prototype is created and tested. We find that the deployable flasher shows promise as an optical array. By following the guidelines in this work, more efficient and powerful optical arrays can be developed.

In this article, we present a comprehensive sensitivity analysis and geophysical retrieval product demonstration to assess the enhanced information content in atmospheric temperature and water vapor, harnessed in hyperspectral microwave measurements. A particular focus of this study is devoted to quantifying and comparing the impact on retrieval performance resulting from novel spectral bands of the microwave thermal spectrum, by means of data addition and data denial trade studies. Various spectral configurations are assessed, each reflecting specific technology solutions intended to maximize geophysical product performance within feasible size, weight, power, and cost constraints. Our results indicate that the use of a hyperspectral sampling in the oxygen and water vapor sounding lines alone provides significant improvements in the lower and free tropospheric thermodynamic fields (up to $\sim$40%), when compared against the program of record (i.e., the Advanced Technology Microwave Sounder, ATMS). Our experiments also demonstrate the essential role played by extending the coverage in the window regions, leading to an overall improvement of up to $\sim$50% in the Earth's planetary boundary layer thermodynamic fields. This work concludes with an overview on the state of the art in hyperspectral microwave technology and a discussion on future applications of interest to numerical weather prediction and climate science. The work presented in this study focuses on ocean, clear-sky demonstrations. All-sky, all-surface investigations will be the focus of a follow-up study, as we advance our capability to simulate more complex scenarios and improve scene variability.