Several studies have found that the vertical Fourier transform of lidar, interferometric Synthetic Aperture Radar (SAR), and stereo photogrammetric profiles at empirically-determined spatial frequencies enables high-performance forest aboveground biomass (AGB) estimation. Linear combinations of real and imaginary parts of Fourier transforms of Tomographic (multi-baseline) SAR (TomoSAR) profiles, from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne data, generate ~20%-precision estimates of AGB in the Saskatchewan area of Canada. We found that this 20% precision can be improved to ~15%, a factor of 30% improvement in root mean square error (RMSE) if, in addition to using Fourier transforms of the profile itself, we use Fourier transforms of the spatial, vertical derivative of the profile. The formulation of this "derivative" algorithm is the subject of this paper.

The ongoing Distributed Aperture Radar Tomographic Sensors (DARTS) project at NASA Jet Propulsion Laboratory aims to mature and demonstrate multi-static SAR measurements for fine-scale 3D imaging of surface topography, vegetation, and surface deformation and change. The project explores the use of drones as SAR platforms and integrates software-defined radar on RF system-on-chip for compact and flexible radar instruments. This paper highlights the progress in DARTS hardware development, experiments, and data processing. The recent experiments have successfully demonstrated monostatic interferometry as well as acquisition and processing of bi-static SAR imagery. By leveraging the advantages of multi-static SAR and drone-based platforms, the project aims to build a testbed for future missions design and enhanced SAR imaging capabilities for scientific applications.

Recent results have highlighted the potential ability of bistatic and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and low-frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response (PTR) as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multiplatform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the PTR may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in single-input–multiple-output (SIMO) mode are the most affected by an oscillator phase noise error, followed by multiple-input–multiple-output (MIMO), with negligible effects on the single-input single-output (SAR-SISO) mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

Recent innovations in small satellite, ultra-wideband direct RF sampling, and synchronization technologies have made multistatic and MIMO coherent SAR constellations a feasible concept for future missions. The Distributed Aperture Radar Tomographic Sensors (DARTS) mission concept at NASA JPL aims to measure Earth's surface topography and vege-tation using TomoSAR techniques. This paper describes the development of an embedded ultra-wideband next generation software defined radar (SDRadar) testbed capable of multi-band operation implemented with the Xilinx RF System on Chip (RFSoC) architecture, which features 8x 6.4 GSPS DACs and 8x 4 GSPS ADCs. The RFSoC SDRadar repre-sents a state of the art testbed for rapid prototyping of radio, radar, and synchronization technologies. We provide preliminary testing results for airborne monostatic radar imaging from a small uninhabited aerial system (sUAS), successfully demonstrating multi-band operation using first and second Nyquist zone direct RF sampling.

The advent of smaller SAR satellites and cheaper access to space is bringing the notion of a multistatic SAR constellation into the realm of feasibility. Researchers at JPL are studying a Distributed Aperture Radar Tomographic Sensors (DARTS) mission concept intended to measure Earth's surface topography and vegetation using TomoSAR techniques. This paper describes progress on the airborne testbed for the DARTS study. The testbed is the union of a software-defined radio that implements a radar and synchronization link together with a small uninhabited aerial system (sUAS) that serves as a platform with precise control of the observation geometry. Initial experiments have demonstrated successful multi-sensor synchronization as well as acquisition and processing of monostatic SAR imagery.

Distributed Aperture Radar Tomographic Sensors (DARTS) is a mission concept being studied at the NASA Jet Propulsion Laboratory in collaboration with the California Institute of Technology to enable global and repeated imaging of surface topography and three-dimensional vegetation structure using single-pass tomographic SAR technique. The observing system consists of a distributed formation of multiple small synthetic aperture radar platforms deployed in space with variable distances to achieve look angle diversity and sensitivity to the vertical distribution of vegetation components. Our goal is to identify the optimal system configuration starting from documented community needs and mature the critical technologies that lead to a viable implementation of DARTS. Here, we provide an overview of DARTS and describe our approach for designing and demonstrating single-pass SAR tomographic systems as part of an on-going funded NASA Instrument Incubator Program effort.

Quantifying Uncertainty and Kinematics of Earthquakes (QUAKES-A) provides an analytic center framework for creating a uniform crustal deformation reference model for the active plate margin of California by fusing InSAR, topographic, and GNSS geodetic imaging data. The objective is to provide tools for sampling spatial processes ranging from local to earthquake faults to broad tectonic deformation and temporal processes ranging from immediately following earthquakes to long-term tectonics. A reference model allows exploration of a uniform model and comparison to new data.

Airborne SAR data are often challenging to analyze due to the limited accuracy of the platform motion measurements. In interferometric scenarios, even small residual motion errors cause undesirable artifacts in the interferometric phase and geometric registration of image pairs. Various calibration techniques have been developed, often exploiting spectral diversity in some way. The UAVSAR L-band system has implemented a new operating mode where multiple images are acquired simultaneously at different azimuth squint angles, providing enhanced opportunities for motion calibration. This paper describes this imaging mode and the residual motion calibration technique developed for these special data sets.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands. The three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR collected data at L and P-bands at Delta Junction, Alaska in September of 2017 whereas the NASA/JPL UAVSAR and DLR F-SAR acquired data at the BERMS site near Saskatoon, Canada on August 19 and 23 of 2018 respectively. Tomographic data sets were collected at L-band and P-band by the NASA/JPL UAVSAR at Delta Junction and at L-band at BERMS and DLR F-SAR acquired data at L-band and S-band. Ground truth data sets and lidar data from the NASA LVIS system were also acquired at BERMS. We compare L and P tomography at Delta Junction and L-band and S-band tomography from the two systems to each other and to the lidar data sets at BERMS. These data are then used to estimate biomass and assess spatial gradients in the canopy vertical structure. We also compare our data with simulated boreal forest data to assess the sensitivity to the data collection geometry and canopy parameters.

Geodetic imaging is useful for measuring topography and motions of the Earth's surface. Geodetic imaging measurements can be used, for example, to measure earthquakes, landslides, debris flows, wildfire extent, volcanos, and anthropogenic changes such as fluid withdrawal from aquifers. Different geodetic measurements sample different parts of the spatio-temporal deformation field. Combining the measurements and analysis improves understanding of a broad range of Earth surface processes. Here we describe a concept to Quantify Uncertainty and Kinematics of Earth Systems (QUAKES) that combines radar interferometry and optical imaging into one airborne platform and the analysis system required to analyze and model the data.

Synthetic aperture radar (SAR) can provide high-resolution imagery regardless of cloud cover or lighting conditions. These qualities make SAR potentially well-suited for informing response efforts to natural and man-made disasters, but such applications require data products with minimal latency. To meet this challenge, we implemented a real-time SAR processor capable of producing 10 m imagery using an NVIDIA Jetson TX2 embedded GPU module. With its low mass (87 g module) and power consumption under 8 W, the system also holds promise for spaceborne applications.

In May-June 2015 UAVSAR was flown to Europe to collect data in support of experiments in Iceland, Norway and Ger-many. The deployment in Germany was focused on PolIn-SAR and tomographic data collections at the Traunstein Forest and in the Munich urban area. In this paper we describe tomographic processing of the Munich data and comparison with in situ ground truth data.

The unique capabilities of imaging radar to penetrate cloud cover and collect data in darkness over large areas at high resolution makes it a key information provider for the management and mitigation of natural and human-induced disasters such as earthquakes, volcanoes, landslides, floods, sinkholes, and wildfires. In 2018 we demonstrated the utility of NASA/JPL's airborne Ka-band single-pass interferometric radar (GLISTIN-A) to monitor the growth of lava flow thickness during the surprisingly extensive Kilauea volcano eruption that lasted 3 months. We also deployed UAVSAR's L-band polarimetric repeat-pass interferometric radar at the request of the Federal Emergency Management Agency (FEMA) to monitor flood extent in heavily vegetated areas of North and South Carolina in the aftermath of Hurricane Florence.

SAR tomographic methods have proven extremely adept at measuring vegetation vertical structure at a variety of wavelengths including L and P-bands [2]. Measuring the three dimensional structure of vegetation and its changes resulting from either natural or anthropogenic causes are key parameters in monitoring ecosystems. The NASA/JPL UAVSAR system has deployed to multiple sites including Alaska over the last several years to conduct tomographic SAR observations at L-band and P-band. This talk will provide a brief overview of a tomographic SAR experiment conducted in the boreal forests of Alaska in August and September of 2017 at both L and P-bands. This site consists mostly of relatively short vegetation with mean height less than 20 m and maximal height less than 25 m. It is sparse compared with previous temperate and tropical forest tomographic observations made by UAVSAR. These observations provides a unique data set to compare tomographic data at L and P-bands for this type of biome.

On September 21 and 22, 2016, the L-band UAVSAR airborne Synthetic Aperture Radar (SAR) imaged the SpaceX rocket debris field at Kennedy Space Center, Cape Canaveral Florida. This debris field was the result of an explosion of a SpaceX Falcon 9 rocket during its launch preparations on September 1. The data collected by UAVSAR allowed us to investigate methods for the detection of small ground targets in a complex wetland environment using multi-aspect, fully polarimetric, and high resolution SAR data. We developed a new time domain processor to focus the SAR data directly to a predefined map grid instead of traditional radar coordinates. This approach allowed us to form co-registered SAR images acquired from 8 flight headings to a common map grid without additional resampling. We computed various polarimetric observables for each observed aspect angle, then calculated statistical deviations to identify candidate locations of debris.

We present our progress results of SAR tomographic imaging using L-band NASA/JPL UAVSAR data collected in Gabon during the 2016 AfriSAR campaign. Several tomographic experiments were conducted in February 2016 over four different sites with a broad diversity of vegetation types, soil characteristics and weather conditions. Here we describe the campaign objectives and report on the status of the UAVSAR tomographic processor for retrieving the 3D structure of forests. We discuss several algorithms, including stack formation, phase calibration and structure retrieval. The availability of NASA/GSFC LVIS waveforms enables cross-comparison of the radar-derived structure with the lidar-derived structure. Results are reported for the Lopé National Park and demonstrate the maturity of the 3D UAVSAR tomographic processing for ecosystem science and applications.

We improved the repeat-pass InSAR processing capability for the L-band UAVSAR airborne synthetic aperture radar in order to support time-series analysis of repeat zero-baseline observations as well as multiple baseline observations for TomoSAR imaging. This new capability enabled us to conduct tomographic experiments in Gabon during the AfriSAR deployment in support of vegetation structure studies. For the GLISTIN-A Ka-band radar, we streamlined the radar operations and implemented a robust production processor that will routinely generate topographic data products in order to support large-scale science campaigns. The new capabilities were put to test in support of the Oceans Melting Glacier Greenland campaign in March 2016.

The NASA/JPL UAVSAR system was deployed to Europe in the May-June 2015 to collect data in support of experiments in Iceland, Norway and Germany. The deployment in Germany was focused on PolInSAR and tomographic data collections at the Traunstein Forest and in the Munich urban area. In addition data were collected at Kaufbeuren, the DLR calibration site, where several surveyed corner reflectors were available for imaging. We describe the experiment design, data collections and present some preliminary results from these experiments.

Uninhabited aerial vehicle synthetic aperture radar (UAVSAR) is a reconfigurable polarimetric L-band SAR that operates in quad-polarization mode and is specifically designed to acquire airborne repeat-track SAR data for interferometric measurements. In this paper, we present details of the UAVSAR radar performance, the radiometric calibration, and the polarimetric calibration. For the radiometric calibration, we employ an array of trihedral corner reflectors, as well as distributed targets. We show that UAVSAR is a well-calibrated SAR system for polarimetric applications, with absolute radiometric calibration bias better than 1 dB, residual root-mean-square (RMS) errors of ~0.7 dB, and RMS phase errors ~5.3°. For the polarimetric calibration, we have evaluated the methods of Quegan and Ainsworth et al. for crosstalk calibration and find that the method of Quegan gives crosstalk estimates that depend on target type, whereas the method of Ainsworth et al. gives more stable crosstalk estimates. We find that both methods estimate leakage of the copolarizations into the cross-polarizations to be on the order of -30 dB.