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A model for the remote sensing of snow water equivalent (SWE) using coherent detection of reflected communication satellite “signals of opportunity” is described in this paper. We modeled the snowpack by a layered medium and compute the phase of specularly reflected signals from the snowpack. Phase change of the reflected signal is predicted to be strongly dependent on SWE for dry snow and on snow depth for wet snow. Phase sensitivity to SWE increases with frequency. Reflected signals with frequencies above S-band, however, will experience rapid phase wrapping (360° change) and become more susceptible to fringe washing due to spatial variability of SWE across the footprint. We also examined the impact of snow grain size, snow density, snow layering, ground surface scattering, and incoherent scattering from ice grains embedded in the snowpack, concluding that these factors do not have a substantial impact on the phase-SWE relationship. However, the snow wetness of more than a few percent can make the reflection from air–snow interface dominant, leading to a correlation between phase change and snow depth. To address the path delay due to the ionosphere, dual-frequency observations would have to be considered. Modeling analysis indicates that the coherent change detection is a promising technique for remote sensing of SWE.

A proof-of-concept experiment was carried out to demonstrate the feasibility of retrieving snow water equivalent (SWE) using P-band signals of opportunity. The fundamental observation is the change in the phase of the reflected waveforms as related to the change in SWE. Through theoretical modeling it was found that the change in SWE was approximately linearly dependent on the change in phase. This was verified by retrieving SWE data collected and processed from a tower-based experiment at Fraser, CO, USA. A linear regression was performed on measured phase and in situ SWE. The correlation was found to be 0.94 and root mean square deviation was found to be 7.5 mm.

The National Aeronautics and Space Administration (NASA) in the United States and the Indian Space Research Organisation (ISRO) are developing an Earth-orbiting science and applications mission that will exploit synthetic aperture radar to map Earth's surface every 12 days, persistently on ascending and descending portions of the orbit, over all land and ice-covered surfaces. The mission's primary objectives will be to study Earth land and ice deformation, and ecosystems, in areas of common interest to the US and Indian science communities. This single spacecraft solution with an L-band (24 cm wavelength) and S-band (10 cm wavelength) radar has a swath of over 240 km at fine resolution, using full polarimetry where needed, uses a reflector-feed system whereby the feed aperture elements are individually sampled to allow a scan-on-receive (“SweepSAR”) capability at both L-band and S-band. This design is in contrast to recent concepts towards large constellations of smaller radar satellites, and is driven by the science requirements for complete coverage over the 12-day repeat cycle, using repeat pass interferometry and polarimetry to measure deformation and surface properties. A single spacecraft with enough aperture, power, duty cycle, and downlink capacity was determined to be a more practical and implementable solution that multiple smaller spacecraft. The use of a single large aperture reflector for both the L- and S-band radars enables both to have comparable performance, leading to overall development and operational efficiencies.

Previous model-based decomposition techniques are applicable to a limited range of vegetation types because of their specific assumptions about the volume scattering component. Furthermore, most of these techniques use the same model, or just a few models, to characterize the volume scattering component in the decomposition for all pixels in an image. In this paper, we extend the model-based decomposition idea by creating an adaptive model-based decomposition technique, allowing us to estimate both the mean orientation angle and a degree of randomness for the canopy scattering for each pixel in an image. No scattering reflection symmetry assumption is required to determine the volume contribution. We examined the usefulness of the proposed decomposition technique by decomposing the covariance matrix using the National Aeronautics and Space Administration/Jet Propulsion Laboratory Airborne Synthetic Aperture Radar data at the C-, L-, and P-bands. The randomness and mean orientation angle maps generated using our adaptive decomposition significantly improve the physical interpretation of the scattering observed at the three different frequencies.

Current polarimetric model-based decomposition techniques are limited to specific types of vegetation because of their assumptions about the volume scattering component. In this paper, we propose a generalized probability density function based on the nth power of a cosine-squared function. This distribution is completely characterized by two parameters; a mean orientation angle and the power of the cosine-squared function. We show that the underlying randomness of the distribution is only a function of the power of the cosine-squared function. We then derive the average covariance matrix for various different elementary scatterers showing that the result has a very simple analytical form suitable for use in model-based decomposition schemes.

Electromagnetic scattering from a rough surface is a function of both surface roughness and dielectric constant of the scattering surface. Therefore, in order to estimate soil moisture of a bare surface accurately from radar measurements, the effects of surface roughness must be compensated for properly. Several algorithms have been developed to estimate soil moisture from a polarimetric radar image, all with limited ranges of applicability. No theoretical algorithm has been reported to retrieve volumetric soil moisture of a vegetated surface. In this paper, we examine a different approach to estimate soil moisture that exploits the fact that the backscattering cross section from a natural object changes over short timescales mainly due to variations in soil moisture. We develop a model function that expresses copolarized backscattering cross sections (sigmahh and sigmavv) in terms of volumetric soil moisture using L-band experimental data for both bare and vegetated surfaces. In order to estimate soil moisture, two unknowns in the model function must be determined. We propose a viable approach to determine these two unknowns using combined radiometer and radar data. This time-series approach also provides a framework to utilize a priori knowledge on soil moisture to improve the retrieval accuracy of volumetric soil moisture. We demonstrate that this time-series algorithm is a simple and robust way to estimate soil moisture for both bare and vegetated surfaces.

The polarimetric backscattering coefficients (vv-, hh-, hv-, and vh-polarizations) of a flooded rice field are measured using L- and C-band ground-based polarimetric scatterometers. These measurements were made during the rice growth cycle, i.e., from the transplanting period to the harvest period (May to October 2006), to understand the feasibility of modeling and estimating rice growth. We also collected ground truth data that include fresh and dry biomasses, plant height, leaf area index, and leaf size. To study the incidence angle effect, the scatterometer data were collected at four different incidence angles, i.e., 30deg , 40deg, 50deg, and 60deg. In this paper, we show that the backscattering coefficients of a rice field can accurately be modeled using the radiative transfer theory. We also demonstrate that a polarimetric scatterometer is an effective tool for estimating rice growth. The hh-polarized backscattering coefficient is more sensitive to rice growth than its vv-polarization counterpart. The polarimetric ratio can be used to estimate rice growth accurately.

The Hydrosphere State Mission (Hydros) is a pathfinder mission in the National Aeronautics and Space Administration (NASA) Earth System Science Pathfinder Program (ESSP). The objective of the mission is to provide exploratory global measurements of the earth's soil moisture at 10-km resolution with two- to three-days revisit and land-surface freeze/thaw conditions at 3-km resolution with one- to two-days revisit. The mission builds on the heritage of ground-based and airborne passive and active low-frequency microwave measurements that have demonstrated and validated the effectiveness of the measurements and associated algorithms for estimating the amount and phase (frozen or thawed) of surface soil moisture. The mission data will enable advances in weather and climate prediction and in mapping processes that link the water, energy, and carbon cycles. The Hydros instrument is a combined radar and radiometer system operating at 1.26 GHz (with VV, HH, and HV polarizations) and 1.41 GHz (with H, V, and U polarizations), respectively. The radar and the radiometer share the aperture of a 6-m antenna with a look-angle of 39/spl deg/ with respect to nadir. The lightweight deployable mesh antenna is rotated at 14.6 rpm to provide a constant look-angle scan across a swath width of 1000 km. The wide swath provides global coverage that meet the revisit requirements. The radiometer measurements allow retrieval of soil moisture in diverse (nonforested) landscapes with a resolution of 40 km. The radar measurements allow the retrieval of soil moisture at relatively high resolution (3 km). The mission includes combined radar/radiometer data products that will use the synergy of the two sensors to deliver enhanced-quality 10-km resolution soil moisture estimates. In this paper, the science requirements and their traceability to the instrument design are outlined. A review of the underlying measurement physics and key instrument performance parameters are also presented.

Polarimetric SAR decomposition has been used extensively because of its capability to identify the strength of each scattering mechanism from polarimetric SAR data. To achieve higher accuracy, one should take into account not only variation of vegetated terrain but also topography under vegetation. In this paper, we firstly isolate vegetation orientation angle from generalized volume scattering component, and then modify a conventional adaptive model-based decomposition by carefully introducing a concept of topographic polarization orientation compensation (TPOC). Finally, experimental validation is conducted by using P-band AIRSAR image of Black Forest in Germany.

A time-series approach is used to estimate the moisture content based on polarimetric SAR data. It is found that under the assumption of constant soil moisture, empirically observed relationships between radar backscatter and moisture are only half as sensitive to moisture as compared to actual radar data. A numerical finite element method is used to calculate the radar backscatter for rough soils with arbitrarily varying soil moisture as a function of depth. Several instance of drying and wetting moisture profiles are considered and the radar backscatter is calculated in each case. Radar backscatter is found to crucially depend on the soil moisture variation in the top half wavelength of soil.

Adaptive model-based Polarimetric SAR decomposition has attracted significant attention to estimate the vegetation parameters of various vegetated terrains. However, an increased flexibility of parameter selection potentially causes unexpected misinterpretation. Using reasonable constraints, we should validate the decomposition results thoroughly based on solid physical interpretation. In this paper, we apply most general adaptive model-based decomposition technique to real SAR data by varying various vegetation parameters. Then validation will be conducted based on physical scattering theory.

Soil moisture inversion from polarimetric SAR data has attracted significant attention for the past twenty years. Comparing with the simple case of bare surface, it is extremely complicated for vegetated terrain to invert soil moisture because of a larger number of scattering mechanisms that contributes to the observation. In this paper, we show how polarimetric decomposition technique, which decomposes SAR observations into preferred scattering mechanisms, can be used for the inversion. The result leads us to give up the use of polarimetric decomposition because of an unknown attenuation ratio caused by the canopy. Then a new inversion algorithm using Polarimetric Scattering Cubes (PSC) is introduced with simulation results to show a sensitivity to physical parameter such as vegetation distribution. Finally, we also discuss how the technique should be implemented for the real SAR data.

The soil moisture active/passive (SMAP) mission is a NASA mission identified by the NRC ldquodecadal surveyrdquo to measure both soil moisture and freeze/thaw state from space. The mission will use both active radar and passive radiometer instruments at L-band. In order to achieve a wide swath at sufficiently high resolution for both active and passive channels, an instrument architecture that uses a large rotating reflector is employed. The active radar will further utilize SAR processing in order to obtain the sub-footprint resolution necessary for the geophysical retrievals. The SMAP radar has a unique geometry where the antenna footprint is continuously rotated about nadir in a conical fashion, as opposed to the more common side-looking SAR design. In support of the design of the SMAP radar, a point target simulator has been constructed and used to validate the SMAP radar design parameters.

In this paper, we will describe the Hydors algorithms to derive soil moisture from L-band polarimetric radar measurements. the baseline Hydros radar algorithm to estimate soil moisture is composed of three steps: land classification, preliminary soil moisture estimation, and final time-series improvement.

This paper focuses on the cross-track interferometric data calibration results and height accuracy analysis. We also present the key elements of the calibration techniques for cross-track interferometric SAR processed with the AIRSAR Integrated Processor.

In this article, we present the basic principles of how remote sensing radars work. These include SARs (synthetic aperture radar), scatterometers, altimeters, sounders, cloud radars, and rain radars. Radars are active sensors; they provide their own illumination and therefore can be operated during day or night. An additional advantage of particularly lower-frequency radars is their ability to penetrate clouds, rain, tree canopies, and even dry soil surfaces. We start the discussion by defining the basic concepts of resolution, the radar equation, signal fading, and the geometric distortions associated with the radar imaging geometry. We also include a description of advanced SAR techniques (polarimetry, interferometry, and polarimetric interferometry) and their applications. We complete the chapter by briefly examining nonimaging radars such as scatterometers, altimeters, radar sounders, and meteorological radars. An extensive list of references is provided for each radar for readers who need an in-depth description of a particular radar. Keywords: remote sensing; radar; SAR; polarimetric SAR; interferometric SAR; polarimetric interferometry; scatterometer; altimeter; sounder; cloud radar; rain radar

[1] We employ L-band airborne synthetic aperture radar (SAR) polarimetry to detect surface changes produced by the Tsaoling landslide, the largest slope failure triggered by the September 1999 Chi-Chi earthquake in central Taiwan. Imaging polarimeters provide a complete description of the scattering properties of radar target materials. Resurfacing of forested hillslopes by landslides alters scattering mechanisms from those dominated by backscatter from trees to mechanisms associated with scatter from rough, bare surfaces. Scattering mechanism information on a per-pixel basis is extracted by decomposing matrices formed as the outer product of the complex scattering vector measured for each resolution cell. We classify surface cover type and thereby identify the extent of the landslide, using such polarimetric parameters as scattering entropy, anisotropy, and pedestal height, derived from the eigenvalues of the decomposition, as well as the weighted average scattering mechanism derived from the eigenvectors. We address the utility of full polarimetry versus dual polarimetry for landslide mapping purposes and show that fully polarimetric SAR is necessary for distinguishing water surfaces of varying roughness from the bare surfaces created by landsliding. We show that scattering entropy and average scattering mechanism, for example, can be used to identify the Tsaoling landslide source, run out area and impounded lakes as proficiently as maps obtained using satellite optical sensors, such as Landsat and the Indian Research Satellite. However, the operational advantages of radar over optical sensing techniques (namely, its day-night, all-weather data acquisition capability) suggest that SAR polarimetry could play a leading role in the rapid assessment of landslide disasters.

Recent advances in polarimetric SAR show promise for augmenting the capability of traditional interferometric SAR. In particular, a polarimetric topography technique provides useful slope information, and polarimetric interferometry may be used to decompose the response into vegetation and ground surface contributions. Here we discuss an integrated approach that utilizes the combined capability of regular (single channel) interferometry, polarimetric interferometry and polarimetric topographic mapping for topographic mapping of flood-prone areas.

Rough surface scattering depends on both roughness and dielectric constant of a surface. Therefore, a polarimetric measurement is useful for estimating soil moisture since we can remove the surface roughness effect using multiple scattering components from a polarimetric measurement. In this paper, we examine the relationship between polarimetric parameters and soil moisture. For example, the co-polarization ratio is independent of surface roughness to the first order of the small perturbation approximation. As surface roughness increases, it can be shown that both co- and cross-polarization ratios depend on the surface slope under the tilted Bragg approximation. The effects of the surface roughness to these ratios are theoretically investigated. An algorithm is proposed to estimate soil moisture from both co- and cross-polarization ratios. We will also study other polarimetric parameters such as the average alpha angle and eigenvalues to understand their relationship with soil moisture.

In this study, we evaluate the effect of the surface roughness on estimation of the relative soil moisture change in repeat-pass L-band radar measurements. It has found the surface roughness has a significant impact and a correction technique has been developed. I. INTRODUCTION During recent years, theoretical modeling and field experiments have established the fundamentals of active microwave remote sensing as an important tool in determining physical properties of soil. In attempt to use active microwave remote sensors in estimation of soil moisture, we are mainly facing two major problems: effects of surface roughness and vegetation cover. There are several algorithms developed for measurement of bare soil moisture quantitatively using dual or three polarization L-band SAR image data. A common idea beyond these algorithms is to separate the effects of the surface dielectric and roughness properties on the backscattering signals to present the model, which the inversion was based on, as a product of a dielectric function and a roughness function. They are first-order statistical inversion models. Depending on the data source, the selection of the surface roughness parameters and the backscattering measurements of the different polarizations or their linear combinations, the models have a great difference in terms of both the dielectric and roughness functions. The temporal variability of surface roughness is generally at much longer time scale than that of soil moisture, unless there was a human activity. Commonly, we can reasonably assume that the surface roughness is same at certain time interval. The change in SAR measurements between the repeat-passes, therefore, is resulted from the change of ground dielectric properties or soil moisture. Therefore, the repeat-pass measurements provide additional relative surface soil moisture change information and make it possible to directly estimate the relative moisture change and improving the accuracy of estimating the bare surface soil moisture. However, there has no quantitative algorithm being developed to estimate the relative soil moisture change using repeat-pass measurements. In this study, we evaluate 1) the effects of surface roughness in L-band repeat-pass measurements using IEM simulated data, 2) developing a quantitative algorithm to estimate relative soil moisture change, and 3) validating this technique with JPL/AIRSAR 92's experiment data over the little Washita test site.

Unlike scalar interferometry, polarimetric interferometry provides the field cross correlation using various polarization responses. The main purpose of implementing polarimetric interferometry is to extract scattering medium information that may be difficult to obtain from scalar interferometry. Even though the formulation and initial demonstrations appear to be very promising, potential applications of polarimetric interferometry can only be verified by comparing polarimetric interferometry signatures with ground truth data. In order to accomplish this, the NASA/JPL TOPSAR system was modified to collect polarimetric interferometry data at C-band. During the 1998 deployment, several data sets were collected in the single pass polarimetric interferometry mode. The authors discuss important issues related to polarimetric interferometry calibration. In addition, the authors briefly discuss the polarimetric interferometric signature using the TOPSAR data.

An L-band radiometer-radar concept has been studied for spaceborne remote sensing of land surface soil moisture, freeze-thaw state, and ocean surface salinity. The integrated design provides simultaneous passive and active measurements with potential for enhanced geophysical retrieval accuracy and spatial resolution. The design takes advantage of cost savings achievable using shared subsystems and hardware. The baseline system concept has been evaluated to determine the feasibility of the technical approach and as a point of departure for system trade-offs. The unique features of this concept are the integration of the radiometer and radar sensors, the use of a deployable-mesh conically scanned reflector antenna, and the use of unfocused synthetic aperture radar (SAR) processing. Taken together, these features represent a significant departure from conventional radiometer, scatterometer, and SAR approaches. The conical wide-swath scan is a desirable feature that provides constant incidence angle and antenna pattern characteristics across the swath, simplified data processing (passive and active), and frequent global sampling. The concept is targeted for a low-cost, short-development-cycle mission, suitable for NASA's Earth System Science Pathfinder (ESSP) series.

It is necessary to calibrate SAR data in order to use the data for science applications. When both polarimetric and interferometric data are collected simultaneously, these SAR data can be used for cross-calibration and verification. The frequency of polarimetric and interferometric data does not have to be the same for this purpose. For example, the NASA/JPL AIRSAR system can acquire C-band interferometric data and L-band polarimetric data simultaneously. The radiometric calibration of polarimetric data can be improved using the local slope information obtained from SAR interferometry. The accuracy of geophysical parameter estimation may be enhanced using true incidence angles derived from SAR interferometry. The calibration of interferometric SAR data can be verified by examining interferometric correlation coefficients. Both azimuth and range slopes estimated from an interferometric SAR DEM can be used to examine the polarization calibration accuracy. In this paper, we show several examples of cross-calibration verification between polarimetric and interferometric SAR data.

Advances in spaceborne Synthetic Aperture Radar (SAR) remote sensing technology make it possible to acquire global-scale data sets that provide unique information about the Earth's continually changing surface characteristics. Short duration missions such as the Spaceborne Imaging Radar-C (SIR-C) and the Shuttle Radar Topography Mission (SRTM) have established the vast potential of SAR for expanding our knowledge of Earth. A long-duration (/spl ges/5 year) free-flying SAR mission is essential to routinely provide valuable information about the dynamic characteristics of our planet. The SAR mission concept, consists of a dual frequency, polarimetric, interferometric system that has broad scientific, environmental preservation, operational, and commercial utility. A feature that greatly reduces the potential for tasking conflicts is the instrument's ability to operate both frequencies independently and simultaneously. The implementation approach includes an innovative government-industry collaboration that has the potential to lead to the creation of new information industries, in a manner similar to the Internet, Global Positioning Satellite (GPS) and commercial space telecommunications.

This paper compares the performance of four bare surface radar soil moisture inversion algorithms in the presence of measurement errors. The particular errors considered include calibration errors, system thermal noise, local topography and vegetation cover.

It is important to monitor forests in order to understand the impacts of global climate changes on terrestrial ecosystems. To characterize forest changes, it is useful to parameterize a forest using several parameters, such as biomass, basal area, tree density, tree height, and trunk diameter. These parameters are not independent and some of them are related by allometric equations. Remote sensing data can be used for estimating some forest parameters and others may be retrieved using allometric equations. Many researchers reported algorithms to estimate forest parameters using polarimetric SAR data. However, these algorithms cannot be applied to all types of forests without additional information on the forest type and environmental conditions since radar measurements depend on the tree structure, incidence angle, and environmental conditions. The backscattering cross section also saturates as forest parameters, such as biomass and the tree height, increase. Forest parameters also have been estimated using SAR interferometry. Specifically, the interferometric correlation coefficient has been used to estimate the angular range of volume scattering. In this paper, we compare and contrast polarimetric and interferometric approaches to understand their advantages and limitations using NASA/JPL AIRSAR data.

SAR (Synthetic Aperture Radar) interferometry has enabled two important science applications: surface change detection and topographic mapping. SAR interferometry is sensitive to the location of the imaged area and the scattering geometry. SAR polarimetry makes use of the polarization dependent scattering response of each pixel within the imaged area. The polarimetric response is highly sensitive to the scattering mechanism of a pixel. S.R. Cloude and K.P. Papathanassiou first published the formulation of polarimetric interferometry that combines both SAR interferometry and SAR polarimetry. The main purpose of using polarimetric interferometry is to extract scattering medium information that may be difficult to obtain from scalar interferometry. Even though the formulation and initial demonstrations appear to be very promising, potential applications of polarimetric interferometry can only be verified by comparing polarimetric interferometry signatures with ground truth data. In this talk, we present the theory and implementation of SAR polarimetric interferometry. Especially, we review SAR polarimetry, SAR interferometry, and SAR polarimetric interferometry in a unified manner. In addition, a new calibration technique suitable for polarimetric interferometry is suggested in this paper.

To enable a low-cost radar mission, various radar techniques and technologies are being developed at many institutions in the world. We summarize several research activities at JPL in this paper.

This article describes the basic principles of radar remote sensing for scientific purposes. In addition, it provides several examples of the radar remote sensing instrument. Scientific remote sensing radar includes synthetic aperture radar (SAR), radar altimeter, scatterometer, meteorological radars, and radar sounder. A radar transmits an electromagnetic signal and receives a reflected echo from illuminated objects. From the received signal, useful information on the illuminated objects can be extracted for various applications. Examples of geophysical quantities that remote sensing radars can measure are land topography, surface deformation, biomass, soil moisture, ocean surface topography, ocean vector winds, cloud, precipitation, and subsurface layers. A radar is capable of collecting remote sensing data under almost all weather conditions, day or night.

During the past ten years, the NASA/JPL AIRSAR system has produced polarimetric and interferometric SAR data. SAR polarimetry is useful for characterizing scattering mechanisms while SAR interferometry yields high resolution topographic maps. For bare surfaces, the interferometric SAR topography represents true ground height. However, the radar height is biased by the volume scattering contribution from tree canopies for vegetated areas. Recently, S.R. Cloude and K.P. Papathanassiou (1997) developed a new radar technique called polarimetric SAR interferometry. This technique can produce a radar topographic map corresponding to a particular scattering mechanism. In this way, both true ground height and tree height may be derived. The NASA/JPL AIRSAR will be upgraded to collect the C-band polarimetric SAR interferometric data in May, 1998. In this work, the hardware modifications required for this upgrade are presented. Since the data are collected in the single pass interferometric mode, one can study various combinations of interferometric/polarimetric data without suffering temporal decorrelation. Both theoretical and preliminary test results are discussed.

In recent years, both polarimetric and interferometric SAR techniques have provided important scientific results. To be useful for science applications, these techniques require precise phase and amplitude calibration. In order to cover large areas using these techniques, it is necessary to use a space-borne SAR. Due to ionospheric disturbance, a space-borne SAR may not be able to produce data useful for science applications. In this paper, the authors present ionospheric effects on polarimetric and interferometric SAR images, especially at lower frequencies. First, they derive analytic equations to investigate ionospheric effects on both range and azimuth compressions. Then, using these equations, they examine important parameters such as pulse dispersion, time delay, and loss of azimuth resolution. They also present possible correction schemes to compensate ionospheric disturbance.

The NASA/JPL AIRSAR Integrated Processor (AIP) is a multi-frequency polarimetric and interferometric SAR processor designed to produce data useful for better understanding of scattering from different types of Earth terrain. The processor is utilized to automatically generate co-registered multifrequency images from both polarimetric and interferometric data collection modes. When one frequency interferometric Digital Elevation Map (DEM) is available, all channel images of three-frequency are projected and terrain-corrected to the DEM. All AIP products are corrected geometrically, polarimetrically, and radiometrically. This paper describes the implementation of the operational AIRSAR Integrated Processor.

The NASA/JPL Airborne Synthetic Aperture Radar system (AIRSAR) has been in operation since 1988. The original radar configuration consisted of PIL/C-band quadpolarization mode in both 20 MHz and 40 MHz chirp bandwidths. Over the years, we have added the L- and C-band along track interferometry mode (ATI), the on-board processor, the C-band cross-track interferometry mode (XTI) in 199 1 , and the L-band XTI mode in 1995. In addition, we also replaced the GPS receiver as well as the inertial navigation system in 1995 to improve the accuracy of motion compensation and geolocation of the output products. In the 1996 PacRim Campaign, we flew a new digital chirp generator that has significantly better chirp linearity, which should improve the ISLR of the output images. In this paper, we will briefly describe the instrument characteristics, the evolution of the various radar modes, the instrument performance and improvement in the knowledge of the positioning and attitude information of the radar. In addition, we will summarize the progress of the data processing effort especially in the interferometry processing. Finally, we will address the issue of processing and calibrating the cross-track interferometry (XTI) data.

To really derive the maximum information content from SAR data, it is desirable to combine polarimetric and interferometric techniques, preferably at multiple frequencies. The NASA/JPL AIRSAR/TOPSAR system is capable of simultaneously acquiring interferometric (C- and/or L-band) and polarimetric data (C-, L- and P-band). The authors describe a processing approach that is used to process data acquired in these modes. This approach is different from that described by Madsen et al. (1993) in both the motion compensation approach, as well as in the sense that data are deskewed before interferograms are formed. Using this approach, the same basic SAR processor is used to process data acquired in any of the modes supported by the AIRSAR/TOPSAR system.

The use of interferometric SAR (IFSAR) to measure elevation is one of the most powerful and promising capabilities of radar. A properly equipped spaceborne IFSAR system can produce a highly accurate global digital elevation map, including cloud-covered areas, in significantly less time and at significantly lower cost than with other systems. For accurate topography, the interferometric measurements must be performed simultaneously in physically sperate receive system, since measurements made at different times with the same system suffer significant decorrelation. The US/German/Italian spaceborne imaging radar C/X-band SAR (SIR-C/X-SAR), successfully flown twice in 1994 aboard the Space Shuttle Endeavor, offers a unique opportunity for global multifrequency elevation mapping by the year 2000. With appropriate augmentation, SIR-C/X-SAR is capable of producing an accurate elevation map covering 80 percent of the Earth's land surface in a single 10-day Shuttle flight. The existing US SIR-C SCANSAR mode provides a 225-km swath at C-band, which makes this coverage possible. Addition of a C-band receive antenna, extended from the Shuttle bay on a mast and operating in concert with the existing SIR-C antenna, produces an interferometric pair. Accuracy is enhanced by utilizing the SIR-C dual polarizations simultaneously to form separate SCANSAR beams. Due to the practical limitation of approximately 60 meters for the mast length, the longer SIR-C L-band wavelength does not produce useful elevation measurement accuracy. IFSAR measurements can also be obtained by the German/Italian X-SAR, simultaneously with SIR-C, by utilizing an added outboard antenna at X-band to produce a swath coverage of about 50 km. Accuracy can be enhanced at both frequencies by processing both ascending and descending data takes. It is estimated that the 90 percent linear absolute elevation error achievable is less that 16 meters for elevation postings of 30 meters. This will be the first use of spaceborne IFSAR to acquire accurate topographic data on a global scale.

The primary purpose of GeoSAR is to demonstrate the feasibility of interferometric topographic mapping through foliage penetration. GeoSAR should become a commercially viable instrument after the feasibility demonstration. To satisfy both requirements, we have designed a dual frequency (UHF- and X-band) interferometric radar. For foliage penetration, a lower frequency (UHF) radar is used. To obtain better height accuracy for low backscatter areas, we proposed a high frequency (X-band) interferometric system. In this paper, we present a possible GeoSAR system configuration and associated performance estimation.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A metrology system to measure the on-orbit movement of a ten meter mast has been built for the Nuclear Spectroscopic Telescope Array (NuSTAR) x-ray observatory. In this paper, the metrology system is described, and the performance is measured. The laser beam stability is discussed in detail. Pre-launch alignment and calibration are also described. The invisible infrared laser beams must be aligned to their corresponding detectors without deploying the telescope in Earth’s gravity. Finally, a possible method for in-flight calibration of the metrology system is described.

The Shuttle Imaging Radar—C(SIR---C) is a synthetic aperture radar(SAR) designed to fly on the Space Shuttle as a payload instrument in the Shuttle Radar Laboratory(SRL). To fly together with SIR-C are the German/Italian X-band SAR(X-SAR) and the Data Processing Subsystem (DPS), built by the Applied Physics Laboratory of John Hopkins University. The first mission is scheduled in mid-April, 1994, for an 8-day duration, with one follow-up mission in planning. The instrument itself — its inheritance, mission profile, potential scientific advances, design and performance — has been published earlier"2

Microwave sensors are of primary importance in mapping surface states and measuring some significant quantities which affect the hydrological cycle. A space mission aiming at monitoring soil moisture and surface salinity at a global scale is suggested. The mission is based on a combination of polarimetric active and passive microwave sensors.