Venus holds the key to understanding rocky planet evolution in our solar system and beyond. As Earth’s "twin" in terms of size, mass and density and sitting within the habitable zone one might expect that the two planets would evolve very similarly. This expectation is quite erroneous with Venus surface and atmospheric conditions being very different than the Earth. To understand how Venus evolved so differently than the Earth ESA and NASA are sending three missions, EnVision, VERITAS and DaVinci to Venus in the 2030s. ESA’s EnVision mission has a synthetic aperture radar, VenSAR, provided by NASA designed to peer beneath the optically opaque atmosphere and provide high resolution imagery and topography of the surface. Here we describe the expected performance of the VenSAR instrument for its various modes of operation.

The Surface Water and Ocean Topography (SWOT) mission was recommended by the 2007 National Research Council Decadal Survey to expand on previous altimetry missions like TOPEX/Poseidon. Utilizing wide-swath altimetry technology, SWOT aims to achieve complete coverage of the world’s oceans and freshwater bodies through high-resolution elevation measurements. SWOT received approval for implementation in 2016, it was ultimately launched in December 2022, and it is currently delivering preliminary data to the public. The primary instrument in SWOT is the Ka-band Radar Interferometer (KaRIn) which utilizes JPL-developed radar interferometry technology to measure ocean and surface water levels with unprecedented accuracy. This paper focuses on the challenges in designing, testing, and finally commissioning in flight a complex instrument like KaRIn. We also present preliminary flight performance and compare it with ground measurements and simulations. Our analysis indicates that KaRIn meets or exceeds all its requirements, but it has also revealed several interesting and unexpected observations, offering just a glimpse of future scientific discoveries that KaRIn will enable.

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.

We introduce a novel RF-photonics receiver concept for high performance ultra-compact 94 GHz radars optimized for cloud and precipitation profiling, planetary boundary layer observations, altimetry and surface scattering measurements. The new receiver architecture offers compelling advantages over traditional electronic implementations, including reduced size, weight and power (SWaP), lower system noise leading to improved sensitivity and a W-band LO with ultra-low phase noise to enable long pulse lengths needed for most compact spaceborne radar systems.

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.

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.

A better understanding of ecosystem processes requires accurate estimates of forest biomass and structure on global scales. Recently, there have been demonstrations of the ability of remote sensing instruments, such as radar and lidar, for the estimation of forest parameters from spaceborne platforms in a consistent manner. These advances can be exploited for global forest biomass accounting and structure characterization, leading to a better understanding of the global carbon cycle. The popular techniques for the estimation of forest parameters from radar instruments, in particular, use backscatter intensity, interferometry, and polarimetric interferometry. This paper analyzes the uncertainty in biomass estimates derived from single-season L-band cross-polarized (HV) radar backscatter over temperate forests of the Northeastern United States. An empirical approach is adopted, relying on ground-truth data collected during field campaigns over the Harvard and Howland Forests in 2009. The accuracy of field biomass estimates, including the impact of the diameter-biomass allometry, is characterized for the field sites. A single-season radar data set from the National Aeronautics and Space Administration Jet Propulsion Laboratory's L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar instrument is analyzed to assess the accuracy of the backscatter-biomass relationships with a theoretical radar error model.

Estimating the amount of above ground biomass in forested areas and the measurement of carbon flux through the quantification of disturbance and regrowth are critical to develop a better understanding of ecosystem processes. Well-resolved and globally consistent inventories of forest carbon must rely on remote sensing measurements, particularly from polarimetric radars. While a wide variety of studies conducted over the past three decades have shown how radar polarimetric measurements can be used to estimate above ground carbon for regions with less than 100 Mg of biomass per hectare, there is no established methodology for assessing biomass estimation accuracy based on a priori instrument and mission parameters. In this paper, a framework for assessing biomass estimation accuracy is presented that is a blend of the basic imaging physics and empirically derived parameters that describe various relationships between biomass and radar polarimetric observable quantities. The implications of this error model on the design and performance of a polarimetric radar are explored using instrument, mission, and science parameters from a notional Earth observing mission.

Quantification of the various components of the carbon cycle budget is key to improved climate modeling and projecting anthropogenic affects on climate in the future. Estimating the levels of above ground biomass contained in the world's forests that comprise 86% of the planet's above ground carbon and monitoring the rate of change to these standing stocks resulting from both natural and anthropogenic disturbances is necessary to solving the carbon cycle sink. Remote sensing is the only viable means of obtaining a global inventory of forest biomass at the hectare scale. The most promising means of obtaining remotely sensed biomass measurements involve using either lidar or radar measurements of vegetation structure coupled with allometric relationships. We have collected repeat-pass L-band fully polarimetric radar data at multiple spatial and temporal baselines to investigate the tree height and structure measurements using polarimetric interferometry techniques. This paper will discuss this experiment and comparison with lidar data.

This paper investigates the ability of radar image segmentation to produce meaningful, structurally homogenous objects with respect to lidar-derived forest metrics. A comparative approach is taken to determine if radar-derived segments perform better in this respect than arbitrary, square segments or landcover-derived segments. It is found that segmentation of UAVSAR co- and cross-polarization backscatter magnitudes results in increased lidar homogeneity on the segment level relative to the arbitrary and landcover segmentations.

The National Research Council's decadal survey recommended DESDynI as one of the high priority missions for NASA. The mission envisions an InSAR/Lidar instrument for observing ecosystem structures on global scales with high spatial resolutions. Consistent and highly resolved global maps of biomass and carbon stocks require highly accurate observations of vegetation, in fact it is expected that such accuracies would require a combination of the high vertical precision of Lidar observations and the large spatial extent of SAR/InSAR measurements. Here we analyze radar backscatter data along with biomass estimates from a field campaign conducted in the Harvard forest in Massachusetts, USA.

The SAR/InSAR component of the NASA DesdynI mission for measuring vertical vegetation structure from space consists of four possible approaches. These include the use of radar backscatter to estimate biomass, to employ PolInSAR relative phase for measuring the vertical extent, the use of interferometric phase and a ground reference, or the use of interferometric correlation magnitude alone. Temporal decorrelation is a significant contributor to decorrelation of interferometric echoes and is not always separable from volumetric decorrelation hence contributing to uncertainties in vegetation parameter estimates obtained using just correlation magnitude. In this text we analyze data that is close to the best case scenario for isolating temporal decorrelation. With almost zero baseline and a repeat pass of one day, SIR-C data over the eastern US serves as our case study of temporal decorrelation.

In this paper, two data sets consisting of co-located full-waveform lidar and InSAR observations are discussed, one over the Duke Forest, near Durham, North Carolina, and the other, the Harvard Forest, located in Western Massachusetts. Data for the Duke forest consists of AIRSAR and GeoSAR (both airborne sensors) interferometric SAR observations spanning in frequency from X-band down to P-band, and data from the GSFC's SLICER instrument. For the Harvard Forest, spaceborne data from JAXA's ALOS/PALSAR mission is used in conjunction with GSFC's LVIS instrument. Early work with SLICER and GeoSAR data has used a lookup table approach for generating a table that correlates the InSAR observables of differential height between X-and P-band observations, and X-band correlation magnitude to lidar derived height. This table was then used for estimating heights over the remaining swath, where lidar data was not available. A similar technique can be used for spaceborne data, in this case, over the Harvard Forest. In this paper, the comparison between lidar observations and the InSAR Duke observations are shown, and then followed by a preliminary treatment highlighting relationships in the ALOS/PALSAR Harvard data that can be exploited for similar purposes.

The recently established National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) will be deploying the first generation of an automated network of four low-power, short-range, X-band, polarimetric, Doppler radars, known as NetRad, in central Oklahoma in late 2005. This network is developed with the goal of tracking tornadoes with high spatial and temporal resolution as well as mapping severe weather events in the lowest 2 km of the troposphere. Each radar node has been developed to accomplish this system goal through the coordinated interaction with other radars in the network via a real-time, closed-loop software control system. This paper will describe the characteristics of the individual radar nodes in the system, with emphasis on those aspects of the design that lend themselves toward operation as a coordinated network. Calibration results and performance characteristics of the single node radar of the first generation system will also be presented.