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.

This paper presents an image formation algorithm for the focusing of SAR data with space-variant impulse response functions caused by eccentric orbits around small high-curvature surfaces, such as is encountered in stable orbits around Saturn’s moon Enceladus, a potential target for future SAR missions such as the Nightingale mission concept under development at JPL. Due to the extreme geometry, the range history shows a significant dependence on the target’s azimuth position within time scales significantly shorter than the synthetic aperture duration. Therefore, additional steps are needed in order to compensate for this effect and minimize image degradation. In this context, the present contribution evaluates the space variance of the geometry for SAR surveys over Enceladus and proposes a processing flow to account for it. Point target simulations using the proposed processing algorithm are shown to verify the approach.

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.

In the framework of the comparison of Synthetic Aperture Radar (SAR) imagery from the Magellan space mission and the VISAR and VenSAR radar instruments which will be onboard the forthcoming VERITAS and EnVision missions to Venus, the problem of the disparity between the resolutions of the images arises when attempting to define a test statistic with which to detect changes. Reliable change detection requires equivalent spatial resolutions which, for the two different images, inevitably involves different equivalent number of looks after speckle-reduction processing. This study presents a method to address this scenario using a Generalized Beta Prime Distribution as a probability density function (PDF) which is fit to the histogram of the ratio between the two intensity images. The work demonstrates and verifies the properties of the function, highlights its most useful traits, and elaborates on the mathematical procedure required to achieve a meaningful change detection in line with the classic theory of equal number of looks. The results show that the method accurately describes the ratio histogram of two SAR intensity images with different number of looks. Furthermore, they demonstrate the adaptability of the method to the presence of high pixel correlation between the images, and validate its robustness in the presence of textural complexity when the texture patterns of the images are similar.

One of the key questions in planetary science is why Venus and Earth, very similar in mass and density, have evolved so differently. The answer to this question has broader implications for rocky planet evolution generally. The Magellan mission in the 1990s imaged the surface of Venus with an S-band synthetic aperture radar over 3 Venus sidereal days and acquired repeated imagery for roughly 42% of the surface. Recent examination of Magellan imagery acquired on Cycles 1 and 2 showed evidence of change to one of the volcanic vents in an area of Alta Regio indicating that Venus is presently volcanically active. Thus, we can expect that the newly selected VERITAS and EnVision missions will provide more examples of Venus geological activity. In this paper we exhibit the Magellan radar imagery of the surface change and describe the simulations used to rule out a false change detection as a consequence of radar vantage differences between observations.

The Venus Interferometric Synthetic Aperture Radar (VISAR) is one of two instruments carried by the VERITAS Discovery Mission to Venus that was selected by NASA in 2021 [1] , [2] . VERITAS (Venus Emissivity, Radio Science, Insar, Topography And Spectroscopy) is a partnership between scientists and engineers at NASA/JPL in an international cooperation with the Germany Aerospace Center (DLR), the Italian Space Agency (ASI) and the French Space Agency (CNES). VISAR aims to be the first to image Venus at 30 m resolution on a global scale and deliver a digital elevation model at 6 m height accuracy in addition to proving interferometric deformation maps of activity on another planet. The VISAR instrument has several interesting features and challenging aspects. The focus is put on SAR performance and the options for the radar operation and imaging mode parameters given the constraints inherent to a planetary mission.

Differential SAR interferometry for deformation measurement and coherent change detection has revolutionized our understanding of many geophysical processes on the Earth since its first demonstration in the 1980’s. The recently selected NASA Discovery mission VERITAS aims to be the first planetary mission to use differential radar interferometric techniques for measuring surface deformation and for coherent change detection measurements. These measurements provide the most sensitive means for detecting and measuring present day geologic activity on Venus. Differential SAR interferometric measurements at Venus by the VERITAS mission has several interesting and unique features including the atmosphere and orbit considerations that are described in this paper.

Eigenvalues and eigenvectors of the polarimetric covariance or coherency matrices play an integral role either directly or indirectly in the analysis and classification of radar polarimetric data. Since these matrices are hermitian their eigenvalues are real and are found simply as the roots of the characteristic polynomial of these matrices. Given an eigenvalue of a matrix a corresponding eigenvector can be found by solving a system of linear equations. There is an interesting and not well known identity, called the eigenvector-eigenvalue identity, that relates the magnitude of the components of an eigenvector to the eigenvalues of the minors of an hermitian matrix. This identity has been rediscovered many times and was recently highlighted in the blog of Fields Medalist Terrance Tao and in a subsequent article. In this paper we illustrate the use of the eigenvector-eigenvalue identity in the context of radar polarimetry using a vegetation model developed by Van Zyl, Arii and Kim. It seems very apropos to dedicate an article emphasizing eigenvectors and eigenvalues in radar polarimetry to the memory of Jakob Van Zyl as these played such a central role in his research.

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.

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.

Venus topography knowledge comes primarily from either Magellan radar altimetry with a spatial resolution of 15-20 km and a elevation accuracy of no better than 100 m or derived from radar stereo data collected by Magellan during Cycles I and III with spatial resolution on the order of half a kilometer and elevation accuracy of 20-50 m that only covered roughly 17% of the surface. We describe a previously unexploited source of Venus topography with intermediate spatial resolution and elevation accuracy to the aforementioned sources thereby extending the available Venus topographic data that uses intra-Cycle stereo observation between adjacent orbit pairs as a source of topographic data. We show that the spatial resolution and elevation accuracy are suitable for use in scientific investigations.

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.

Utilizing digital multi-channel technology, spacebome synthetic aperture radar instruments are capable of imaging swath widths of hundreds of kilometers at fine azimuth resolution. The main benefit follows through the extension of the trade space and the use of new digital beam-forming techniques facilitated through the multi-channel instrument architecture. This is truly a quantum leap as the performance of these systems will be orders of magnitude better than current in-orbit and state-of-the art systems. One of the basic restrictions applicable to spaceborne platforms hosting both the transmitter and receiver is the "blinding" of the receiver during the transmit time instances, which manifests itself through imaging gaps. One of the main challenges the instrument designers are faced with, is to circumvent these gaps, requiring the use of dedicated instrument operation modes. An alternative approach is multi-beam imaging, i.e. to allow the gaps in the single SAR acquisition, while using an appropriate mission design for filling the blind gaps. This paper explores the trade space options for high-resolution wide-swath SAR imaging. The comparison of multi-beam and gapless imaging from an instrument design and performance point of view is elaborated.

Interferogram filtering for noise reduction is a key to many radar interferometric applications. Repeat pass radar interferometry often uses data with less than ideal correlation levels resulting from either long spatial or temporal baselines or changes between observations leading to high levels of temporal correlation. To maximize the utility of such pairs filtering the interferogram to get maximal noise reduction is often needed. One technique that has proved quite useful in the geophysical community is power spectral or Goldstein-Werner filtering of the interferogram whereby a power-weighted version of the Fourier transform is used to enhance fringe visibility. Although this paper defining the filter briefly touched upon the spatial resolution and noise reduction induced by the filter, it did not provide a useful formula for predicting the phase noise after filtering. This paper derives a formula for the phase noise obtained from power spectral filtering albeit under the restriction of several simplifying assumptions to make the problem analytically tractable. In particular, it is assumed that the interferometric phase is locally well approximated by a linear phase ramp with nonlinear phase perturbations small in a spectral energy sense compared to the linear term.

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.

We study the temporal variability of soil and vegetation backscatter at L-band using a dense airborne time-series. Backscatter is assumed to change over time due to diurnal variations in soil and canopy water content as well as precipitations. A two-layer SAR backscattere model traditionally used for above-ground biomass retrieval is augmented here with the time dimension in order to guide the data analysis. The model is informed by examining a 5-year time-series of 32 L-band polarimetric UAVSAR images acquired over a vegetated area near the Sacramento Delta in California. Our initial results reported in this paper show that the temporal variability of soil and vegetation backscatter in absence of precipitation events fits well a lognormal probability distribution with mean and standard deviation related to each other. Characterizing the diurnal, seasonal and interannual variability of L-band backscatter may be critical for the successful estimation of ecosystem variables from dense time-series to be acquired globally by the NISAR mission.

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.

Background The AfriSAR campaign was a joint NASA and European Space Agency airborne campaign conducted in Gabon in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The aim of the campaign was to collect ground, airborne SAR and airborne Lidar data for the development and evaluation of forest structure and biomass retrieval algorithms. The campaign consisted of two deployments, the first in 2015 with the ONERA SETHI SAR system and the second in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR and the DLR F-SAR. In addition, field teams from the Gabon ANPN (Agence Nationale des Parcs Nationaux), University College London and NASA were collecting ground data. Here we focus on the 2016 NASA contributions to campaign.

Interferogram filtering for noise reduction is a key to many radar interferometric applications. Repeat pass radar interferometry often uses data with less than ideal correlation levels resulting from either long spatial or temporal baselines or changes between observation leading to high levels of temporal correlation. To maximize the utility of such pairs filtering the interferogram to get maximal noise reduction if often needed. One technique that has proved quite useful in the geophysical community is power spectral or Goldstein-Werner filtering of the interferogram whereby a power weighted version of the Fourier transform is used to enhance fringe visibility [1]. Although this paper briefly touched upon the spatial resolution and noise reduction induced by the filter it did not provide a useful formula for predicting the expected amount of noise reduction. The purpose of this paper is to present a formula for the amount of noise reduction obtained from power spectral filtering albeit under the restriction of several simplifying assumptions to make the problem analytically tractable.

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.

This paper discusses the evaluation of a state-of-the-art Ka-band (35.7GHz) single-pass interferometric synthetic aperture radar (InSAR) for snow-depth mapping during the NASA SnowEx experiment. The use of InSAR for this application presents a new approach and potential alternate technology to lidar with the advantage of wide-swath operation that is not hampered by cloud cover. We discuss the plans to leverage SnowEx for calibration, in particular characterizing potential biases due to interferometric penetration into the snow-cover by comparison with lidar and in situ measurements. In addition, lidar from the Airborne Snow Observatory will be used for validation of tree classifications and the derived digital surface models and snow-depth maps.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing a synthetic aperture radar (SAR) mission to map Earth's surface every 12 days, known as the NASA-ISRO SAR (NISAR) Mission. NISAR has completed its preliminary design and successfully passed its Preliminary Design Review in June 2016. This paper describes the radar instrument design, engineering model hardware development status, test results, and the plans for future maturation toward the Critical Design Review in late 2018. NISAR has two radars sharing a mechanical structure and reflector, one operating at L-band (24 cm wavelength) and the other at S-band (10 cm wavelength). To achieve wide-swath observations at both wavelengths, NISAR is designed as a reflector-feed system where the feed aperture elements are individually sampled to allow a scan-on-receive capability. In the partnership, NASA provides the instrument structure for both L- and Sband electronics, the L-band electronics, the reflector and associated boom, and an avionics payload to interface with the radar including a solid-state data recorder, high-rate Ka-band telecommunication link, and a GPS receiver. In addition to providing the spacecraft and launch vehicle for the mission partnership, ISRO is also providing S-band radar electronics, and an additional high-rate Ka-band telecom package. This paper will describe aspects of this technically and logistically complex international instrument and mission development, one that requires careful definition of interfaces, tracking of resources, joint analysis of designed capabilities, and detailed integration and test plans.

This letter presents the first demonstration of millimeter-wave single-pass interferometric synthetic aperture radar (InSAR) for snow-depth mapping. Maps are presented over the Tuolumne River Basin region of the Sierra Nevada, CA, USA, and compared with those collected by a scanning lidar onboard the NASA Airborne Snow Observatory for the same region on the same snow day. For this observation, the snow surface was wet and melting and as such penetration of the electromagnetic wave into the snow volume can be effectively neglected. Despite the rugged terrain, heavy tree-cover, and very low snow-volume, depth maps had a standard deviation <;1 m with the largest differences occurring on slopes exceeding 40°. While additional evaluation is needed with demonstration of the InSAR capability over a greater range of conditions and terrain, these results are promising. InSAR for snow-depth mapping holds significant advantages for a spaceborne mission if proven viable as it can operate through cloud cover, day or night, and measure snowpack when wet or melting.

Surface cross-track clutter can corrupt both earth and planetary radar sounder (RS) observations preventing definitive interpretation of subsurface features, which are often of primary interest to geologists and planetary scientists. This clutter is usually identified either by manual or automatic techniques that require ancillary information about the topography of the surface, or by using multichannel RS systems with arrays of antennas. However, topographic information is not always available and multichannel systems are generally too massive and costly to mount on satellites for the planetary exploration. In this paper, we propose a novel approach to clutter discrimination that is independent of ancillary information and limits the hardware complexity of the RS system. This approach uses a two-channel RS and exploits cross-channel interferometric phase differences to discriminate the clutter. Our approach includes three main steps: 1) manual feature extraction and theoretical phase-difference estimation; 2) RS interferogram formation; and 3) comparison of theoretical and real phase difference distributions. The proposed method was validated on RS data acquired in Greenland and provides a proof of concept for the surface clutter discrimination using RS data.

Differential interferometric synthetic aperture radar (DInSAR) measurements are sensitive to displacements, but also to soil moisture mνchanges. Here, we analyze whether soil moisture can be estimated from three DInSAR observables without making any assumptions about its complex spatio-temporal dynamics, with the goal of removing its contribution from the displacement estimates. We find that the referenced DInSAR phase can be a suitable means to estimate mνtime series up to an overall offset, as indicated by correlations with in situ measurements of 0.75-0.90 in two campaigns. However, the phase can only be referenced when no displacements (and atmospheric delays) occur or when they can be estimated reliably. We study the separability of displacements and mνusing two additional DInSAR observables (closure phase and coherence magnitude) that are sensitive to mνbut insensitive to displacements. However, our analyses show that neither contains enough information for this purpose, i.e., it is not possible to estimate mνuniquely. The soil moisture correction of the displacement estimates is hence ambiguous too. Their applicability is furthermore limited by their proneness to model misspecifications and decorrelation. Consequently, the separation of soil moisture changes and displacements using DInSAR observations alone is difficult in practice, and-like for mitigating tropospheric errors-additional data (e.g., external mνestimates) or assumptions (e.g., spatiotemporal patterns) are required when the mνeffects on the displacement estimates are comparable to the magnitude of the movements. This will be critical when soil moisture changes are correlated with the actual displacements.

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.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing a synthetic aperture radar (SAR) mission to map Earth's surface every 12 days, known as the NASA-ISRO SAR (NISAR) Mission. NISAR has two radars sharing a mechanical structure and reflector, one operating at L-band (24 cm wavelength) and the other at S-band (10 cm wavelength). To achieve wide-swath observations at both wavelengths, NISAR is designed as a reflector-feed system where the feed aperture elements are individually sampled to allow a scan-on-receive capability. In the partnership, NASA provides the instrument structure for both L- and S-band electronics, the L-band electronics, the reflector and associated boom, and an avionics payload to interface with the radar including a solid-state data recorder, high-rate Ka-band telecommunication link, and a GPS receiver. ISRO provides the spacecraft and launch vehicle, and the S-band radar electronics, and an additional high-rate Ka-band telecom package. Hardware prototyping has matured designs for engineering models, which are currently under development.

Change detection using synthetic aperture radar (SAR) data is useful in emergency situations and unfavorable weather conditions. In this letter, change detection using multitemporal polarimetric Uninhabited Aerial Vehicle SAR data is investigated in an urban environment. The most robust polarimetric parameters are determined, and change detection techniques using a maximum likelihood ratio and a hyperbolic tangent model function are applied to the selected parameter. The model function was introduced to quantify the change characteristics and to rule out seasonal changes or those related to mobile features, and thus to only detect durable and permanent changes in urban environments. A comparison of results with historical Google Earth images showed a good level of agreement. Fitting of the hyperbolic tangent function to the multitemporal polarimetric parameters significantly reduces the false detection rate and indicates whether a building was constructed or destroyed, as well as when the detected changes occurred.

Radiometric correction of radar images is essential to produce accurate estimates of biophysical parameters related to forest structure and biomass. We present a new algorithm to correct radiometry for 1) terrain topography and 2) variations of canopy reflectivity with viewing and tree-terrain geometry. This algorithm is applicable to radar images spanning a wide range of incidence angles over terrain with significant topography and can also take into account aircraft attitude, antenna steering angle, and target geometry. The approach includes elements of both homomorphic and heteromorphic terrain corrections to correct for topographic effects and is followed by an additional radiometric correction to compensate for variations of canopy reflectivity with viewing and tree-terrain geometry. The latter correction is based on lookup tables and enables derivation of biophysical parameters irrespective of viewing geometry and terrain topography. We evaluate the performance of the new algorithm with airborne radar data and show that it performs better than classical homomorphic methods followed by cosine-based corrections.

Surface clutter can corrupt both Earth and planetary Radar Sounder (RS) observations preventing definitive interpretation of subsurface features, which are often the primary interest of geologists and planetary scientists. Clutter is usually detected by manual or automatic techniques that require ancillary information about the topography of the surface. However, this topography information is not always available. In this paper, we propose a novel method for clutter detection that is independent from ancillary information. This method uses a two channel RS system to exploit the cross-channel interferometric phase difference and is made up three main steps: i) feature extraction and theoretical phase difference estimation, ii) RS interferogram formation and iii) comparison of theoretical and real phase difference distributions. The proposed method has been validated on RS data acquired in Greenland.

In order to measure the response of the Slumgullion landslide to hydraulic forcing, we utilize the unique capabilities of the NASA/JPL's UAVSAR airborne repeat-pass SAR interferometry system to provide surface geodetic measurements with “landslide-wide” spatial coverage. Unlike traditional space-based INSAR we are not restricted to fixed flight tracks or fixed repeat times, allowing for optimal imaging geometries and timing. We combine four look directions chosen based on the landslide geometry and invert for the full 3-D landslide-wide surface deformation. These observations complement ongoing GPS measurements and in-situ observations of pore-pressure and atmospheric parameters acquired by the U.S. Geological Survey.

SAR change detection is useful when emergency situations occurred and weather conditions are unfavourable. In this study, a change detection using multi-temporal polarimetric UAVSAR data was investigated in urban environment. The most robust polarimetric parameter was evaluated, and change detction techniques using maximum likelihood ratio and hyperbolic tanget function were applied to the selected parameters. The comparison results with Google Earth's historical images showed a quite good agreement. A fitting of hyperbolic tangent function to the multi-temporal polarimetric parameters much reduced the false alarm rate, and it also provide whether the building was constructed or destructed and when the the changes occurred.

Mechanical properties of glacier beds impose fundamental constraints on glacier flow across a wide range of timescales [1]. Despite their importance in governing glacier dynamics, basal mechanics are not well understood, particularly where glaciers are underlain by deformable till [2]. While some till samples have been retrieved from beneath several glaciers and tested in laboratories in order to ascertain till rheology [3, 4], limitations on clast sizes imposed by apparatus dimensions and the difficulty of understanding and reproducing subglacial environments in the lab necessitate observations of the mechanical properties of in situ tills [5]. Such observations are sparse, owing to the inherent difficulty in attaining them, and this observational paucity has helped foment persistent uncertainties concerning the proper rheology of subglacial till and the rheological dependence on mechanical, thermal, and hydrological forcing [1, 2].

We propose a planar-layered medium-based radar backscat-ter model to predict coherence trends, in Interferometric Synthetic Aperture Radar (InSAR) images, manifest when interrogating planar-layered dielectric geophysical subsurfaces. This InSAR coherence model improves upon past ones in two ways: Incorporation of “multi-bounce”, arising from the guidance behavior of minimally-attenuating and high-contrast dielectric slabs, as well as azimuthal deviation in antenna pointing. Including the former renders this model especially suitable for analyzing coherence modifications (decorrelation and phase bias) arising from remote sensing of low-attenuating targets (e.g., dry soil), which augments applicability to high-attenuating targets (e.g., wet, salty soils) readily analyzed with many traditional InSAR models. Representing the model's key contributions, we discuss two predicted trends: Namely, in the limit of a perfectly guiding dielectric slab (i.e., zero attenuation and infinite dielectric contrast), the interferometric correlation is inversely proportional to the InSAR perpendicular baseline length and the phase bias linearly diverges.

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.

This paper addresses the important yet unresolved problem of estimating forest properties from polarimetric-interferometric radar images affected by temporal decorrelation. We approach the problem by formulating a physical model of the polarimetric-interferometric coherence that incorporates both volumetric and temporal decorrelation effects. The model is termed random-motion-over-ground (RMoG) model, as it combines the random-volume-over-ground (RVoG) model with a Gaussian-statistic motion model of the canopy elements. Key features of the RMoG model are: 1) temporal decorrelation depends on the vertical structure of forests; 2) volumetric and temporal coherences are not separable as simple multiplicative factors; and 3) temporal decorrelation is complex-valued and changes with wave polarization. This third feature is particularly important as it allows compensating for unknown levels of temporal decorrelation using multiple polarimetric channels. To estimate model parameters such as tree height and canopy motion, we propose an algorithm that minimizes the least square distance between model predictions and complex coherence observations. The algorithm was applied to L-band NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar data acquired over the Harvard Forest (Massachussetts, USA). We found that the RMS difference at stand level between estimated RMoG-model tree height and NASA's lidar Laser Vegetation and Ice Sensor tree height was within 12% of the lidar-derived height, which improved significantly the RMS difference of 37% obtained using the RVoG model and ignoring temporal decorrelation. This result contributes to our ability of estimating forest biomass using in-orbit and forthcoming polarimetric-interferometric radar missions.

Estimation of forest height from combined polarimetric and interferometric synthetic aperture radar (Pol-InSAR) measurements has been the focus of radar remote sensing studies in the past decade. The simplicity of the random-volume-over-ground (RVoG) model makes it one of the most widely used candidates for estimating canopy height. However, the polarization-independent extinction coefficient assumption in the RVoG model fails in some certain types of the canopies, as suggested by the oriented-volume-over-ground (OVoG) model. The sensitivity of coherence magnitude and phase to different parameters of the canopy is expressed in a closed-form formulation in this paper for the first time. In order to simplify our formulation, the forest is represented by a layer of discrete randomly distributed dielectric scatterers over ground, with azimuthal symmetry. The sensitivity analysis of this work quantifies the contribution of differential extinction due to polarization change in interferometric coherence. Therefore, we can quantitatively evaluate whether the RVoG model is accurate enough to be used for a specific kind of canopy or the OVoG model is needed for better estimation. A simple layer of leaves over ground is used to simulate the sensitivity of Pol-InSAR measurements to different parameters.

A one-dimensional digital beam-forming (DBF) algorithm that is suitable for onboard real-time implementation is described in this article. A couple of techniques for generating a set of DBF coefficients will be also proposed to improve radar system performance such as signal-to-noise ratio (SNR) and/or range ambiguity (as a part of multiplicative noise ratio, MNR), and range impulse response parameters including compression gain, range resolution, etc. Results from simulations demonstrate improvement obtained by these techniques at the output of digital beamforming process. Finally, via the proposed onboard DBF algorithm, a processed synthetic aperture radar (SAR) image generated from data of airborne SweepSAR experiment [1] is exhibited.

Changes in soil moisture between the two radar acquisitions can impact the observed coherence 7 in differential interferometry: both correlation |γ| and phase φ are affected. The influence on the latter potentially biases the estimation of deformations. These effects have been found to be variable in magnitude and dependent on polarization, as opposed to predictions by existing models. Such diversity can be explained when the soil is modelled as a half-space with spatially varying dielectric properties and a rough interface. The first-order perturbative solution achieves - upon calibration with L band data - median correlations ρ at HH of 0.77 for the phase Φ and of 0.50 for |γ|. The depth distribution of the scattering heterogeneities within the soil impacts the sensitvity of the observables to soil moisture changes in a way similar to a changing relative importance of the surface and the volume components, thus leading to similar qualities of fit when the latter is estimated. The first-order expansion does not predict any impact on the HV coherence, which is however empirically found to display similar sensitivities to soil moisture as the co-pol channels. These results indicate that the first-order solution, while not able to reproduce all observed phenomena, can capture some of the more salient patterns of the effect of soil moisture changes on the HH and VV DInSAR signals.

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.

Since the 2007 National Academy of Science “Decadal Survey” report “Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond” [1], the National Aeronautics and Space Administration (NASA) has been studying concepts for a Synthetic Aperture Radar (SAR) mission to determine Earth change in three disciplines - ecosystems, solid earth, and cryospheric sciences. One of the most promising and original concepts involves an innovative international partnership between NASA and the Indian Space Research Organization (ISRO). Previous NASA concepts had focused on exploiting an L-band array-fed reflector SAR configuration that enabled > 200 km swath at full SAR resolution and full polarimetry simultaneously in order to meet requirements in all three disciplines [2]. The feed where the electronics are housed in this design is relatively compact compared to a planar phased array antenna with similar azimuth resolution capability. This compactness allows for straightforward addition of feed array elements at other frequencies. As the partnership concept with ISRO developed, it became clear that flying dual L- and S-band SAR capabilities, with L-band electronics supplied by NASA and S-band electronics by ISRO, would satisfy science and application requirements of the US and India. A dual-frequency fully polarimetric SAR with the potential for global coverage every 12 days would offer unprecedented capability that researchers could exploit in new and exciting ways.

Different mechanisms for the impact of soil moisture on interferometric radar data have been proposed, but its magnitude, sign and even presence have barely been studied empirically and thus remain poorly understood. In this study the dependence of the phase and coherence magnitude on soil moisture was inferred empirically with regression techniques: this was done for two airborne data sets at L band. The phase dependence was significant (α = 0.05) for more than 70% of the fields at HH polarization, its sign corresponding to an increase in optical path upon wetting. This trend was similar in both campaigns, whereas the prevalence of soil moisture-related decorrelation differs. These results are only consistent with a dielectric origin of the soil moisture effects, and not with soil swelling or the penetration depth hypothesis.

The National Aeronautics and Space Administration's (NASA) proposed Soil Moisture Active Passive (SMAP) satellite mission ( ~ 2014) will include a radar system that will provide L-band multi-polarization backscatter at a constant incidence angle of 40°. During the pre-launch phase of the project, there is a need for observations that will support the radar-based soil moisture algorithm development and validation. A valuable resource for providing these observations is the NASA Jet Propulsion Laboratory Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). However, SMAP will observe at a constant incidence angle of 40°, and UAVSAR collects data over a wide range of incidence angles (25°-60°). In this investigation, a technique was developed and tested for normalizing UAVSAR data to a constant incidence angle. The approach is based on a histogram matching procedure. The data used to develop and demonstrate this approach were collected as part of the Canadian Soil Moisture Experiment 2010 (CanEx-SM10). Land cover in the region included agriculture and forest. Evaluation was made possible by the acquisition of numerous overlapping UAVSAR flight lines that provided multiple incidence angle observations of the same locations. Actual observations at a 40°incidence angle were compared to the normalized data to assess performance of the normalization technique. An optimum technique should be able to reduce the systematic error (Bias) to 0 dB and to lower the total root mean square error (RMSE) computed after correction to the level of the initial residual error (RMSEres) present in the data set. The normalization approach developed here achieved both of these. Bias caused by the incidence angle variability was minimized to ~ 0 dB, whereas the residual error caused by instrument related random errors and amplitude fluctuations due to ground variability was reduced to approximately 3 dB for agricultural areas and 2.6 dB for forests; these values were consistent with the initial RMSEresestimated using the un-corrected data. The residual error can be reduced further by aggregating the radar observations to a coarser grid spacing. The technique adequately adjusted the backscatter over the full swath width irrespective of the original incidence angle, polarization, and ground conditions (vegetation cover and soil moisture). In addition to providing a basis for fully exploiting UAVSAR (or similar aircraft systems) for SMAP algorithm development and validation, the technique could also be adapted to satellite radar systems. This normalization approach will also be beneficial in terms of reducing the number of flight lines required to cover a study area, which would eventually result in more cost-effective soil moisture field campaigns.

Rapid response to natural disasters resulting from events such as earthquakes, volcanoes, floods, and tsunamis or anthropogenically induced events such as oil spills often requires response time measured in hours to days. The type of information required spans information on the magnitude and location of damage needed by immediate response teams to longer time scale information to monitor recovery efforts. Airborne radar can play an important role in response to disasters given its day/night and all weather imaging capability coupled with its unique set of measurement observables such as millimeter level surface deformation from radar interferometry and polarimetric scattering data. The properties a radar sensor must possess to have a useful role, e.g., frequency, resolution, swath width, etc., depends on the intended application. In this paper we discuss the potential for radar like the NASA/JPL UAVSAR system to respond to disaster management and provide examples from existing UAVSAR data collection to illustrate its potential.

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.

In this paper we show our first POLINSAR results using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) developed by the Jet Propulsion Laboratory (JPL). UAVSAR is a L-band repeat-pass polarimetric and interferometric system designed for measuring vegetation structure and monitoring crustal deformations. In order to extract canopy height from POLINSAR data and account for temporal decorrelation, we formulate a physical model of the temporal-volumetric coherence, random motion over ground (RMOG) model. Canopy height extracted from single-baseline UAVSAR data using the RMOG model is shown to be in agreement with canopy height measured by the Land, Vegetation, and Ice Sensor (LVIS) lidar.

The world forest systems are dynamic and play an integral role in the Earth's carbon budget. Monitoring of these valuable assets is being mandated by the international community. The requirement of global forest inventories suggests that a global measurement methodology should be adopted and that a verification and validation strategy should be accepted. The wide areas and varied forest types that need monitoring suggest the use of airborne remote sensing assets even for verification or cross validation of the varied global forest measurement methodologies. Airborne SAR systems that operate at appropriate frequencies, e.g., L-band or P-band, can provide useful forest information. The simplest forest product to generate would be a forest/non-forest classification map that can be robustly generated using radar polarimetric or interferometric systems. More elaborated products like forest classification or biomass maps require more sophisticated mapping algorithms and potentially ancillary data sets in order to obtain robust results. In this paper we examine the potential for airborne mapping system to obtain these type of forest mapping products and the limitations and accuracy of such systems.

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.

This paper describes comparison of digital elevation data derived from synthetic aperture radar (SAR) and stereo optical imagery used to understand temporal changes of tropical forest and to estimate tree height in tropical regions. Tree height information is important for forest management and global carbon cycle studies. An intereferometric SAR (InSAR) potentially unhampered by tropical cloud cover. InSAR heights depend on the microwave frequency: low frequency P-band penetrates to the forest floor and ground-volume scattering yields a height close to terrain height, high frequency X-band displays only shallow canopy penetration, and may yield heights several metres below the canopy surface. The exact behaviours depend on the nature of both terrain and forest. A stereo-optical sensor may be used to derive digital elevation data using photogrammetry. Several recent satellite-based stereo-optical instruments operate with fine spatial resolution however optical imagery is affected by clouds. The forest height derived from optical images is essentially the top of the canopy. This research investigates whether it is possible to understand forest changes in tropical regions by comparing digital elevations from dual-frequency, single-pass airborne InSAR with satellite-based stereo-optical data in Papua New Guinea. The results indicate the possibility of forest change and tree height estimation using this combination of data.

This paper describes a physical model of the temporal changes that occur in vegetated land surfaces observed by a repeat-pass radar interferometer. We assume the temporal changes to be caused by a Gaussian-statistic motion of the vegetation elements, with motion variance changing along the vertical direction. We show that the temporal correlation between two interferometric radar signals is affected by the structural parameters of the vegetation, such as canopy height, and varies with the wave polarization. We validate the model using L-band data acquired by the Jet Propulsion Laboratory with the Uninhabited Aerial Vehicle Synthetic Aperture Radar airborne radar. This work provides new insights into the role of temporal decorrelation in interferometric radar applications.

Soil Moisture Active Passive (SMAP), a proposed mission in support of the Earth Science Decadal Survey, conducted afield campaign in June 2010 to support algorithm development. As part of the experiment in situ soil moisture measurements were made over a two week period in which multiple UAVSAR flights were conducted. Repeat-pass polarimetric-interferometric data generated from these flights were analyzed to see if phase changes could be correlated with soil moisture changes. Also, we compared the data to that predicted by simple surface scattering models and showed moderate agreement with the Oh model [4].

The islands of the Sacramento-San Joaquin Delta have been subject to subsidence since they were first reclaimed from the estuary marshlands starting over 100 years ago, with most of the land currently lying below mean sea level. This area, which is the primary water resource of the state of California, is under constant threat of inundation from levee failure. Since July 2009, we have been imaging the area using the quad-polarimetric UAVSAR L-band radar, with eighteen data sets collected as of April 2011. Here we report results of our polarimetric and differential interferometric analysis of the data for levee deformation and land surface change.

To better prepare data processing system of the Ka-band Radar Interferometer (KaRIn) for the Surface Water and Ocean Topography (SWOT) [1] mission, we opportunistically collected data over several diverse fresh-water targets in the Van Hook Arm areas of North Dakota, USA, using a Ka-band interferometric radar [2] developed at JPL. To make the collection relevant to SWOT, the aircraft was rolled to direct the antenna boresight toward nadir to mimic the SWOT geometry. Using a modified airborne interferometric SAR processor [3] developed at JPL, we were able to process the collected Ka-band airborne data and produced the height and magnitude image products. These results are the first of the kind for Ka-band interferometric synthethic aperture radar over water surfaces with near nadir looking geometry. These initial results will help to characterize the power returns from water surface and land, and provide guidance for the design of post-processing algorithms including methods for water and land classification. The height accuracy we get from the water surface height images can be used to project the water surface height accuracy we would get from SWOT mission. This airborne experiment will help us verify the SWOT data processing chain and make us better prepared for SWOT data processing task.

As part of the NASA International Polar Year activities, a Ka-band cross-track interferometric synthetic aperture radar (SAR) recently demonstrated high-precision elevation swath mapping capability. This proof-of-concept instrument was achieved by interfacing two Ka-band slotted-waveguide antennas in a cross-track geometry and Ka-band electronics with the Jet Propulsion Laboratory's L-band uninhabited aerial vehicle SAR. Deployed on the NASA Gulfstream III, initial engineering flights in March and April 2009 marked the first airborne demonstration of single-pass cross-track interferometry at Ka-band. Results of a preliminary interferometric assessment indicate height precisions that, for a 3 m × 3 m posting, range from 30 cm in the near range to 3 m in the far range and greater than 5 km of swath over the urban areas imaged. The engineering flights were followed by a comprehensive campaign to Greenland in May 2009 for ice-surface topography mapping assessment. Toward that end, coordinated flights with the NASA Wallops Airborne Topographic Mapper lidar were conducted in addition to establishing ground calibration sites at both the Summit Station of the National Science Foundation and the Swiss Camp of the Cooperative Institute for Research in the Environmental Sciences. Comparisons of the radar-derived elevation measurements with both in situ and lidar data are planned for a subsequent paper; however, at this stage, a single data example over rugged ice cover produced a swath up to 7 km with the desired height precision as estimated from interferometric correlation data. While a systematic calibration, including assessment and modeling of biases, due to penetration of the electromagnetic waves into the snow cover has not yet been addressed, these initial results indicate that we will exceed our system requirements.

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.

Tandem-L is a proposal for an innovative interferometric and polarimetric radar mission that enables the systematic monitoring of dynamic processes on the Earth surface. Important mission objectives are global forest height and biomass inventories, large scale measurements of millimetric displacements due to tectonic shifts, and systematic observations of glacier movements. The innovative mission concept and the high data acquisition capacity of Tandem-L provide a unique data source to observe, analyze and quantify the dynamics of a wide range of mutually interacting processes in the bio-, litho-, hydro- and cryosphere. By this, Tandem-L will be an essential step to advance our understanding of the Earth system and its intricate dynamics.

At present there is no consensus as to which remote sensing technologies are appropriate for tropical forest biomass estimation. Cloud cover in the tropics and biomass saturation suggest that a combination of low-frequency SAR and interferometry (either PolInSAR or dual-band interferometric SAR DBInSAR) could provide a solution. Tropical forest biomass recovery using X-P DBInSAR has been demonstrated from an airborne platform using the X-P DEM height difference. This height is known to be considerably lower than the tree height as a result of penetration of microwaves into the canopy that can be significant even at X-band. We model the penetration using the RVOG model and show that in the strong attenuation approximation the interferometric coherence magnitude can be used to estimate penetration depth. We compare the model with GeoSAR DBInSAR observations of Peruvian forest, and, by comparison with LiDAR data, show that the GeoSAR Xband interferometric height can be corrected towards the upper canopy using knowledge of the coherence magnitude combined with the high-frequency "X-RVOG" model. We employ the corrected height with a biomass inversion equation derived from plot samples covered in the Peru campaign and generate a map of above ground forest biomass.

We present a new interferometric synthetic aperture radar (InSAR) processing approach that capitalizes on the precise orbit tracking that is available with modern radar satellites. Our method uses an accurate orbit information along with motion-compensation techniques to propagate the radar echoes to positions along a noninertial virtual orbit frame in which the location and focusing equations are particularly simple, so that images are focused without requiring autofocus techniques and are computed efficiently. Motion compensation requires two additional focus correction phase terms that are implemented in the frequency domain. If the images from an interferometric pair or stack are all computed along the same reference orbit, flat-Earth topographic correction is not needed, and image coregistration is simplified, obviating many difficulties that are often encountered in InSAR processing. We process several data sets collected by the ALOS PALSAR instrument and find that the geodetic accuracy of the radar images is 10-20 m, with up to 20 m of additional image distortion needed to align 100 km × 100 km scenes with reference digital elevation models. We validated the accuracy by using both known radar corner reflector locations and by the registration of the interferograms with digital maps. The topography-corrected interferograms are free from all geometric phase terms, and they clearly show the geophysical observables of crustal deformation, atmospheric phase, and ionospheric phase.

GeoSAR is a unique dual-band, interferometric SAR (DBInSAR) sensor capable of collecting single-pass, X-band (VV) and P-band (HH) interferometric data simultaneously. In this paper we examine the dependence of the P-band HH interferometric phase centre height upon forest and terrain parameters. We develop a simple model for P-band GeoSAR observations, and use the model to show how the elevation in P-band HH phase centre height above true ground height is related to the volume-to-ground scattering ratio. GeoSAR is not fully-polarimetric, but records cross-polar (HV) returns at P-band (although not interferometrically). We conjecture that these returns are dominated by direct-volume scattering and related to the direct-volume HH backscatter. We use this relationship to model the dependence of the P-band HH DTM height upon the HV/HH ratio, and the difference in X-band DEM with P-band DTM heights. The relationships are examined using simulated forest InSAR data, and a model is proposed for ground-height and tree-height estimation using DBInSAR that does not require full polarimetry.

Tropical forests host some 40% of the world's above-ground vegetation biomass. Tropical forest biomass estimation from remote sensing is a key issue for REDD and carbon market credit allocation and monitoring. At present there is no consensus on the appropriate remote sensing technologies for tropical forest areas. Cloud cover in the tropics and biomass saturation suggest that a combination of low-frequency SAR and interferometry (either PolInSAR or dual-band interferometric SAR DBInSAR) can provide a solution. The airborne GeoSAR collects X-band and P-band InSAR data simultaneously, at a rate of 288 sq km / minute, and is used for wide-area mapping. Tropical forest biomass recovery using X-P DBInSAR and P-band backscattering cross section has been demonstrated from an airborne platform. The technique is applied to GeoSAR data of tropical forests. We show that GeoSAR X-P interferometric data alone may be used to recover tropical forest biomass, removing ambiguity associated with variation in ground conditions. The effects of terrain slope on biomass recovery are discussed. Airborne observation would yield only a "snapshot" of biomass and carbon stocks. We suggest that a combination of GeoSAR observation with PALSAR data for forest/non-forest classification, plus natural sequestration modelling, should provide an accurate measure of tropical forest biomass temporal variation at high-spatial resolution.

The Cassini RADAR is a multimode instrument used to map the surface of Titan, the atmosphere of Saturn, the Saturn ring system, and to explore the properties of the icy satellites. Four different active mode bandwidths and a passive radiometer mode provide a wide range of flexibility in taking measurements. The scatterometer mode is used for real aperture imaging of Titan, high-altitude (around 20 000 km) synthetic aperture imaging of Titan and Iapetus, and long range (up to 700 000 km) detection of disk integrated albedos for satellites in the Saturn system. Two SAR modes are used for high- and medium-resolution (300-1000 m) imaging of Titan's surface during close flybys. A high-bandwidth altimeter mode is used for topographic profiling in selected areas with a range resolution of about 35 m. The passive radiometer mode is used to map emission from Titan, from Saturn's atmosphere, from the rings, and from the icy satellites. Repeated scans with differing polarizations using both active and passive data provide data that can usefully constrain models of surface composition and structure. The radar and radiometer receivers show very good stability, and calibration observations have provided an absolute calibration good to about 1.3 dB. Relative uncertainties within a pass and between passes can be even smaller. Data are currently being processed and delivered to the planetary data system at quarterly intervals one year after being acquired.

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 National Research Council's Decadal Survey for Earth Science identified InSAR (Interferometric Synthetic Aperture Radar) observations among the highest priorities for new NASA Earth missions. A system making observations required by the solid Earth, vegetation, and ice/climate science communities is recommended. In response, analyses are underway to evaluate efficient combinations of science objectives and mission/instrument scenarios. The InSAR component can be satisfied by a new radar instrument concept capitalizing on existing technology and hardware, including a large commercial mesh reflector antenna and transmit/receive modules developed for the UAVSAR airborne radar. This InSAR system satisfies key science objectives and addresses several shortcomings of existing InSAR capable satellites. To reduce temporal decorrelation, L-Band (23 cm) wavelength is used. A 300 km wide-swath scanSAR mode with 8 day repeat enhances study of ice dynamics, pre/post earthquake deformation, volcano monitoring, and other dynamic phenomena. With a minor orbit change, global biomass surveys are possible using multipolarization. Key challenges are involve scheduling to optimize conflicting observational requirements of various science communities served.

Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

A design constraint traceable to the early days of spaceborne synthetic aperture radar (SAR) is known as the minimum antenna area constraint for SAR. In this paper, it is confirmed that this constraint strictly applies only to the case in which both the best possible resolution and the widest possible swath are the design goals. SAR antennas with area smaller than the constraint allows are shown to be possible, have been used on spaceborne SAR missions in the past, and should permit further, lower-cost SAR missions in the future.

A methodology, example and accuracy assessment are given for a continental-scale mosaic of the Amazon River basin at 100 m resolution using the JERS-1 satellite. This unprecedented resource of L-band SAR data collected by JERS-1 during the low-flood season of the river amounts to a collection of 57 orbits of the satellite and a total of some 1500 1 k/spl times/1 kB images. Interscene overlap both in the along-track and cross-track directions allows common reference points to be used to correct individual scene geolocation inaccuracies that have been derived from the satellite ephemeris. The set of common reference points is assembled into a matrix formulation that is used to solve for individual scene geometric offsets. By correcting for these offsets, each scene is placed within a global coordinate system, which can then be used as the basis for creating a final, visually seamless mosaic. The methodology employed in this approach allows for a mathematical foundation to be applied to the mosaicking process as well as providing a unique, traceable solution for correctly geolocating satellite imagery.

This paper revisits the problem of finding a parametric form for the rain drop size distribution (DSD) which (1) is an appropriate model for tropical rainfall, and (2) involves statistically independent parameters. Using TOGA/COARE data, the authors derive a parametrization which meets these criteria. This new parametrization is an improvement on the one that was derived by Z. S. Haddad et al. (1996) using TRMM ground truth data from Darwin, Australia. The new COARE data allows the authors to verify that the spatial variability of the two "shape" parameters is relatively small, thus confirming that this parametrization should be particularly useful for remote sensing applications. They also derive new DSD-based radar-reflectivity-rain-rate power laws, whose coefficients are directly related to the shape parameters of the DSD. Perhaps most important, since the coefficients are independent of the rain-rate itself, and vary little spatially, the relations are ideally suited for rain retrieval algorithms. It should also prove straightforward to extend this method to the problems of estimating cloud hydrometeors from remote-sensing measurements.

An interferometric radar technique for topographic mapping of surfaces promises a high-resolution approach to the generation of digital elevation models. The authors present analyses of data collected by the synthetic aperture radar instrument on-board the ERS-1 satellite on successive orbits. Use of a single satellite in a nearly repeating orbit is attractive for reducing cost and spaceborne hardware complexity; also it permits inference of changes in the surface from the correlation properties of the radar echoes. The data have been reduced to correlation maps and digital elevation models. The correlation maps show that temporal correlation decreases significantly with time, but not necessarily at a constant well-defined rate, likely depending on environmental factors. When correlation among passes remains high, however, it is possible to form digital elevation models. Analyses of noise expected in ERS-1 interferometric data collected over Alaska and the southwestern United States indicate that maps with relative errors less than 5 m rms are possible in some regions. However, orbit uncertainties imply that tie points are required in order to reduce absolute height errors to a similar magnitude. The authors find that about 6 tie points per 40/spl times/40 km scene with 5 m rms or better height accuracy are needed to keep systematic map height errors below 5 m rms. The performance of the ERS-1 radar system for topographic applications, though useful for a variety of regional and local discipline studies, may be improved with respect to temporal decorrelation errors and absolute height acuity by modifying the orbit repeat period and incorporating precise orbit determination techniques. The resulting implementation will meet many, but not all, objectives of a global mapping mission.>