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.

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 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.

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.

LiDAR remote sensing systems are deployed in various platforms including satellites, airplanes, and drones — which, in essence, determines the sampling characteristics of the underlying imaging system. Low-altitude LiDARs provide high photon count and high spatial resolution but only in very localized patches. Satellite LiDARs, on the other hand, provide measurements at a global scale but are limited by low photon count and their samples are sparsely apart along swath line trajectories that are far in between. This paper describes a new class of satellite remote sensing LiDARs, aimed at overcoming the limitations of current satellite imaging systems. It exploits the principles of compressive sensing and machine learning (ML) to compressively sense Earth from hundreds of km above Earth to then reconstruct the 3D imagery with resolution and coverage, as if the data was collected from airborne platforms at just hundreds of meters in height.We introduce a novel representation of waveform altimetry profiles, coined HyperHeight Data Cubes (HHDC), which encompass rich information about the 3D structure of a scene. Canopy height models, digital terrain models, and many other features of a scene that are embedded in HHDC are easily extracted with simple statistical quantiles.We introduce machine learning methods to reconstruct the compressive LiDAR measurements so as to attain high-resolution, dense coverage, and broad field-of-view per swath pass. ML training data is attained from NASA’s G-LiHT imaging missions. Simulations with various types of forests across the US illustrate the power of the new LiDAR imaging systems.

We present a comprehensive Earth Planetary Boundary Layer temperature and water vapor retrieval improvement demonstration by the use of hyperspectral microwave measurements. 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 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 so called spectral window regions, leading to an overall PBL temperature and water vapor improvement of up to 50%.

We have developed a process for evaluating future satellite-based hyperspectral microwave sensor designs using deep neural networks (DNN). Our approach combines a sophisticated simulated data product with a hierarchical deep neural network capable of comparing the relative performance of a variety of different microwave sounder configurations. These configurations include both spectral band coverage and resolution which allows for a thorough investigation of the solution space. The relative performance between these configurations as tested on the prediction of the planetary boundary layer height (PBLH) is used to perform the evaluation. We plan to extend this method to the prediction of entire temperature and water profiles to further refine this process.

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.

This paper presents an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a 2021 NASA Instrument Incubation Proposal funded project aimed at developing the very first hyperspectral microwave sensor to augment thermodynamic sounding capability from space, with a focus on the Earth's Planetary Boundary Layer. This research responds to the recommendation expressed in the 2018 National Academies of Sciences decadal survey to accelerate the readiness of high-priority PBL observables not feasible for cost-effective spaceflight in 2017–2027. This paper provides an overview on HyMPI's design, configured as the objective instrument concept needed to fly in the future PBL mission and presents preliminary trade studies aim at demonstrating HyMPI's enhanced thermodynamic sounding skill in the Earth's Planetary Boundary Layer over conventional microwave sounders from the current Program of Record.

We present an overview of the Hyperspectral Microwave Photonic Instrument (HyMPI), a NASA Instrument Incubation Proposal funded research project aimed at developing a hyperspectral microwave instrument intended for enhanced remote sensing of atmospheric temperature and water vapor from space. This paper provides preliminary results on HyMPI's spectral and noise characteristics and a preliminary demonstration of its enhanced water vapor sensitivity and vertical resolution, with a particular focus on the Earth's Planetary Boundary Layer.

We present the design and performance of an adaptive wavelength scanning lidar (AWSL) for highly efficient mapping from low Earth orbit (LEO). Mapping is accomplished by steering a laser beam across 1,200 resolvable spots using wavelength tuning and grating dispersion. Any subset of these 1,200 spots can be selected by wavelength switching. The design is validated with an 1550-nm prototype using a fast wavelength-tuned (500-kHz) pulsed (2-ns) fiber laser with the beam dispersed by gratings for beam steering. Reflected pulses are detected with an eight-pixel detector array with single-photon sensitivity. Eight lidar returns are time-multiplexed to one output that is digitized with a single 1-GSPS-digitizer, to save power. A grating spectrometer rejects solar background noise spatially and spectrally, and images laser footprints on to the detector array. The gratings retain the fiber laser beam quality. We are developing a 1030-nm AWSL intended for a LEO SmallSat platform.

We present recent progress on a photonic integrated lidar system for carbon dioxide (CO2) active remote sensing at 1572.335 nm. With integration, the cost, size, weight and power (CSWaP) of the system are significantly improved. System and subsystem level results are demonstrated.

We review integrated photonics technology and the applications for space-based remote-sensing. We cover the state of the technology and its advantages in a variety of sensing applications, discussing some active programs implementing integrated photonic solutions and showing the potential for improved systems performance.

At NASA Goddard Space Flight Center, we have been developing spaceborne lidar instruments for space sciences. We have successfully flown several missions in the past based on mature diode pumped solid-state laser transmitters. In recent years, we have been developing advanced laser technologies for applications such as laser spectroscopy, laser communications, and interferometry. In this article, we will discuss recent experimental progress on these systems and instrument prototypes for ongoing development.

We present recent progress on a wavelength tunable, pulsed laser source for laser spectroscopy of CO2at 1572 nm. An integrated photonics design in indium phosphide (InP) is presented and compared to a fiber-component-based implementation. Significant improvement in size, weight and complexity is demonstrated.

NASA's Goddard Space Flight Center has been developing lidar to remotely measure CO2and CH4 in the Earth's atmosphere. We have advanced the tunable laser technology to enable high-fidelity measurements from space. In this paper, we will report on the progress of a fiber-based, 1.57-micron wavelength, laser transmitter that has demonstrated the optical performance required for a low earth orbiting instrument. The Laser transmitter has been packaged and is undergoing environmental testing to demonstrate its technology readiness for space.

Lasers designed for space-based applications must meet stringent performance requirements, but be robust enough to survive severe vibrational launch levels, and operate reliably on orbit for years with no maintenance. The transmitters that are currently in use in the CALIPSO mission and that are being designed for the ICESat-2 mission are good examples of this type of laser. We use our experiences in the design, build, and qualification testing of these lasers to illustrate our approach to space-flight laser development.

We report progress in assessing the feasibility of a new satellite-based laser-sounding instrument to measure CO2concentrations in the lower troposphere from space.

We report on the tunable solid-etalon filter used in ICES at/GLAS 532 nm channel lidar receiver.