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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.

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.

Several authors have proposed decompositions of measured polarimetric covariance matrices into the weighted sums of individual covariance matrices that represent known scattering models. In this paper we critically examine some minimum requirements that these model-based polarimetric scattering decompositions must satisfy. Starting with the definition of the covariance matrix, we show that a necessary requirement for these decompositions is that the eigenvalues of the individual covariance matrices must be non-negative. We then show with examples that this is not guaranteed for some of the proposed decompositions. Finally, we propose a method of decomposition that would ensure that the eigenvalues of the individual covariance matrices would be non-negative.

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.

In this paper, we present an integrated, end-to-end synthetic aperture radar (SAR) processing system that accepts data processing requests, submits processing jobs, performs quality analysis, delivers and archives processed data. This fully automated SAR processing system utilizes database and internet/intranet web technologies to allow external users to browse and submit data processing requests and receive processed data. It is a cost-effective way to manage a robust SAR processing and archival system. The integration of these functions has reduced operator errors and increased processor throughput dramatically.

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.

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.

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.

This paper presents an analysis with numerical examples of the ionospheric effects on a P-band (500 MHz) SAR system. The results show that the azimuthal resolution for P-band SAR at both 400 km and 600 km altitude in an average ionosphere (30 TECU, /sup 1/2//N=10%) can be seriously affected. For the range resolution at P-band, the image shift is significant, pulse broadening is negligible, and the multiple scattering effect on range resolution is also negligible. The Faraday rotation effect is very significant at P-band, but the ellipticity change is negligible, and the orientation angle change is the main contribution to the Faraday rotation.