current status and future developments in radar remote sensing

1SPRS Journal of Photogrammetry and Remote Sensing, 47 (1992) 79-99 79 Elsevier Science Publishers B.V., Amsterdam

Current status and future developments in radar remote sensing

Diane L. Evans Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 911009, USA

(Received February 8, 199l; revised and accepted April 11, 1991 )

ABSTRACT

Evans, D.L., 1992. Current status and future developments in radar remote sensing. ISPRSJ. Photo- gramm. Remote Sensing, 47: 79-99.

This paper briefly outlines some of the major initiatives and directions of remote sensing using Synthetic Aperture Radar (SAR) data alone and in conjunction with other sensors for earth science investigations. Specific emphasis is on areas key to global monitoring using SAR data from spaceborne platforms: calibration, geophysical processing, and generation of digital elevation models. Cal- ibration as used here encompasses end-to-end system characterization over the life of a sensor and characterization of data products relative to past and future sensors. Geophysical processing is defined here to include any processing which results in derived geophysical units. Acquisition of an additional data type, topography, is required to complete the three-dimensional view of surface properties and correct for distortions inherent in SAR. Methodologies must be developed in each of these critical areas before SAR data can be used routinely for process studies. Additional challenges include development of strategies to extrapolate from regional to global-scale models and development of new sensor technology.

1. INTRODUCTION

Sensors cu r ren t ly flying, and p l anned for flight in the 1990's, will p ro v id e m u l t i p a r a m e t e r syn the t ic ape r tu re r ada r ( S A R ) s ignatures o f the ea r th ' s surface and c ove r at a var ie ty o f scales (Tab le 1 ). T h e c o m b i n a t i o n o f the Sovie t A L M A Z , E u r o p e a n Space Agency (ESA) E u r o p e a n R e m o t e Sensing Satel- lites (ERS-1 and ERS-2 ), the Japanese Ear th Resources Satell i te (J-ERS-1 ), Shutt le Radars ( S I R o C / X - S A R ) , and the Canad ian Radarsa t will p rov ide da ta ove r a va r ie ty o f inc idence angies at L- (wave leng th ~ 2 5 c m ) , C- (wavelength ~ 5 c m ) and X - b a n d (wave leng th ~ 3 c m ) in a var ie ty o f po la r i za t ion combina t ions . Miss ions such as the Ear th Observ ing Sys tem ( E O S ) SAR, p l a nne d for la ter in the decade , will e x p a n d on these systems an d will p r o v i d e bo th global coverage and a m o n i t o r i n g capabil i ty . W h e n c o m b i n e d wi th da t a acqu i r ed wi th ear l ier SAR satell i tes (e.g., Seasat, SIR-A and SIR-B) it will be

0924-2716/92/$05.00 © 1992 Elsevier Science Publishers B.V. All fights reserved.

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TABLE 1

Future spaceborne SAR missions

I).[. EVANS

Parameter ERS-I SIR-C/X-SAR JERS-I RADARSAT EOSSAR ALMAZ 1,2

Spectral coverage C L, C, X L C L, C, X S Polarization VV Quad (C, L) HH HH Quad (I,) HH

VV (X) Dual (C, X ) Look angle (deg) 23 15-60 35 20-50 15-40 30-60 Resolution (m) 30 25 30 10-100 20-250 15-30 Swath coverage (km) 100 10-150 80 50/500 30-500 20 Orbit altitude (km) 800 215 568 792 620 300 Orbit inclination (deg) 98 57 98 98.6 98 73 Launch date 1991 1993 1992 1994 1999 1990

1994 1996

possible to build up a time-series view of temporal change over many regions of the earth.

The purpose of this paper is to briefly outline some of the major initiatives and directions of remote sensing using SAR data alone and in conjunction with other sensors for Earth Science investigations. Specific emphasis is on areas that are key to global monitoring using SAR data from spaceborne platforms: calibration, geophysical processing, and generation of digital elevation models (DEMs).

2. CALIBRATION

Fundamental to derivation of many geophysical quantities is sensor calibration. Calibration as used here encompasses end-to-end system characterization over the life of a sensor, and characterization of data products relative to past and future sensors. It includes internal and external calibration, generation of standardized data products and metadata with consistent nomen- clature and definitions, and maintenance of data and information systems which contain all pertinent information about system performance, sensor and platform stability, processing parameters, etc.

2.1 Imaging radar polarimetry

Radar image brightness is related to radar backscatter at the pixel level, and is a function of slope, surface roughness, dielectric constant and subsurface discontinuities. It therefore provides intrinsic physical information about surfaces and volumes that complements measurements made by sensors op- erating in the visible, short-wave infrared and thermal infrared portions of the spectrum.

CURRENT STATUS AND FUTURE DEVELOPMENTS IN RADAR REMOTE SENSING 81

A new class of imaging radar data, radar polarimetric data, has recently become available, which makes it possible to infer more detailed information regarding the geometric structure of surfaces and cover than is possible from image brightness alone. This new data type, however, has specific calibration requirements that have not been defined previously for imaging radar systems. Conventional imaging radars operate with a single, fixed-polarization antenna for both transmission and reception. A single scattering coefficient is measured, for a specific transmit and receive polarization combination and the scattered wave, a vector quantity, is measured as a scalar quantity. In a polarimetric radar system, to ensure that all the information in the scattered wave is retained, the polarization of the scattered wave is measured through a vector measurement process, which permits measurement of the full polarization signature of every resolution element in an image. The ability to cal- culate the cross section of a scatterer for any assumed transmit and receive polarization combination leads to a less ambiguous solution for the electrical properties and geometric shape of a surface scatterer.

The Jet Propulsion Laboratory (JPL) developed and flew the first imaging radar polarimeter in 1985. The basic datum measured by a polarimeter is a complex (amplitude and phase) scattering matrix for each very small resolution element of the radar. For reasons of data handling efficiency, several individual measurements are usually combined to form the Stokes matrix (Van Zyl, 1985; Van Zyl et al., 1987) corresponding to that group of pixels. Further data volume reduction can be achieved through a data compression scheme described by Dubois and Norikane ( 1987 ).

Polarization signatures, which are plots of the power of a scattered wave as a function of transmit and receive polarizations (van Zyl et al., 1987; Zebker et al., 1987 ) provide a way to visualize the polarimetric scattering properties of a surface. The pedestal height, which is the minimum of a polarization signature, has been found to be proportional to the amount of incoherent scatter (van Zyl and Zebker, 1990). In addition to the usual co- and cross- polarized polarization signatures for which both transmit and receive polarizations are specified, Evans et al. ( 1988 ) introduced polarized and unpolar- ized signatures which depict the relative power of the return wave for all transmit polarizations. These signatures, taken together, represent a more complete description of the polarimetric scattering properties of a surface than has been previously achieved.

2.2 Polarimetric calibration requirements

Different investigations involving the use of SAR data have significantly different calibration requirements. In general, those which emphasize photo- interpretation only require that contiguous image frames be radiometrically balanced. Investigations aimed at ( 1 ) derivation of quantitative geophysical

8 2 l ) i . EVANS

information (e.g. surface roughness and dielectric constant ); (2) monitoring daily or seasonal changes in the Earth's surface or cover (e.g. soil moisture or plant biophysical parameters); and ( 3 ) extension of local case studies to regional or world-wide scales using a single data set, or a variety of sensors, on the other hand have much stricter requirements.

Dubois et al. (1989) described an approach to deriving polarimetric calibration requirements based on a suite of investigations planned during the SIR-C/X-SAR missions. They used a model to simulate the effect of miscal- ibration on parameters which are commonly extracted from SAR data, including polarization signatures such as those described in the previous section. Requirements specific to polarimetric studies are (1) polarimetric amplitude unbalance (a), which is defined as the mismatch in amplitude between the horizontally transmitted, horizontally received (HH); vertically transmitted, vertically received (VV); horizontally transmitted, vertically received (HV); and vertically transmitted, horizontally received (VH) channels: (2) polarization phase calibration (arg(a)) , defined as the mismatch in phase between HH, VV, HV and VH; and ( 3 ) cross-polarization error ( e ), defined as the relative amplitude of cross-polarized signal to like-polarized signal. Additional radiometric calibration parameters applicable to all systems were also investigated. These include: ( 1 ) absolute calibration, defined as the uncertainty in the estimation of the backscatter coefficient from the SAR image intensity as a result of system-induced errors; and (2) relative calibration, defined as the uncertainty in the ratio of two backscatter values in the SAR images. Relative calibration is further separated into ( 1 ) short- term (i.e. within a scene), which provides radiometric fidelity across an image and requires a limit on artifacts introduced by antenna pattern or processor and accurate knowledge of antenna pointing; and (2) long-term (i.e. hours/days/months/years) , which provides repeatable images for multiple passes over identical scenes acquired on separate passes or during different missions. This requires good stability of system gain, antenna pointing and image processing. Finally, since SIR-C/X-SAR is a multifrequency sensor, an across frequency calibration (the ability to compare the ratios of L-band to C-band in different areas within a scene and from scene to scene ) is specified. The resulting calibration goals resulting from this study are given in Table 2.

2.3 Operational calibration

Several techniques have recently been developed to calibrate the NASA Airborne Imaging Radar (AIRSAR) which may make it possible to opera- tionally calibrate the parameters listed in Table 2. External calibration of relative amplitude, relative phase, absolute amplitude, and system cross-talk using the radar return from natural targets and at least one trihedral corner reflector was described by van Zyl (1990). The calibration technique in-

CURRENT STATUS AND FUTURE DEVELOPMENTS IN RADAR REMOTE SENSING

TABLE 2

Summary of SIR-C calibration goals

83

Long- and short-term relative calibration Absolute calibration Across frequency calibration Polarimetric amplitude unbalance Polarimetric relative phase calibration Polarimetric crosstalk error

+1 dB +3 dB + 1.5 dB + 0.4 d B (two way) 10 ° (two way ) - 30 dB

Signal to noise ratio 20 dB

volves four steps: ( 1 ) phase calibration using the phase equalization based on an area where the H H - V V phase difference is known (Zebker and Lou, 1989); (2) cross-talk calibration which assumes that the cross-talk parameters are reasonably small and that the average co- and cross-polarized com- ponents of the scattering matrix are uncorrelated; (3) co-polarized channel amplitude and phase imbalance calibration using trihedral corner reflectors deployed in the scene prior to imaging; and (4) an overall radiometric calibration using the measured absolute radar cross section of the trihedral corner reflectors in the scene.

Results from the first two calibration steps for a study area in Lunar Crater, Nevada, are shown in Fig. 1. Figure 2 shows polarization signatures for corner reflectors deployed in this area before and after calibration. It should be noted that this technique is valid for most geologic targets. In areas where this method may not be valid (i.e., where a target has some preferential orienta- t ion), corner reflectors may be replaced by active radar calibration devices (Freeman et al., 1990).

Work is currently ongoing to integrate external calibration information with internal calibration data decommutated from aircraft or spacecraft telemetry into a calibrated SAR processor. Data processed in such a system would be generated along with a look-up table of the relationship between pixel values and backscatter coefficient. This look-up table would be included in a calibration data record as outlined by the SAR Data Standards subgroup of the Com- mittee on Earth Observing Sensors (CEOS) Working Group on Data which has established standard formats for SAR data products and specifications for computer compatible tape (CCT) formats.

CEOS has also established a Working Group for Calibration which empha- sizes intercalibration of current and future sensors. ERS-1 will provide the first opportunity to assess the feasibility of long-term calibration of a spaceborne SAR. If such calibration can be achieved, it will allow continuous monitoring capability over sites of interest through J-ERS-l, SIR-C/X-SAR, Ra- darsat and EOS SAR.

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In addit ion to spaceborne SAR calibration, the CEOS Working Group for Calibration has provided a focus for aircraft calibration campaigns. Several countries participated in calibration campaigns in 1989 and 1991 during the NASA DC-8 deployments to Europe. As part of the 1989 campaign the AIR-

CURRENT STATUS AND FUTURE DEVELOPMENTS IN RADAR REMOTE SENSING 8 5

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SAR C-band VV channel (vertically transmitted polarization, vertically received polarization) was intercalibrated with the German Aerospace Estab- lishment (DLR) E-SAR system to within 1 dB for most targets (Heel et al., 1990). Additional aircraft calibration campaigns are planned for Brazil and Australia.

3. GEOPHYSICAL P R O C E S S I N G

Geophysical processing is defined here in the most general sense to include any processing or post-processing which results in derived geophysical units. Automation of these steps is critical if SAR data are to be used for global- scale monitoring and mapping studies.

3.1 Background

Determination of sea ice motion is perhaps the most advanced operational use of SAR geophysical processing. Automated geocoding, ice motion, classification and change detection algorithms have been implemented at the Alaska SAR Facility (ASF) in preparation for ERS-1 (Curlander et al., 1985; Kwok et al., 1990; Holt et al., 1989, 1990; Kwok et al., 1990). Automated processing steps in the ASF Geophysical Processor System (GPS) include image pair selection, feature extraction, feature matching, area-based matching, consistency checks, filtering of bad matches, and motion field database update (Kwok et al., 1990).

Current research focuses on the development of geophysical processing for extraction of additional global change parameters such as surface roughness, biomass, and soil moisture; and improvement of geophysical processing for ice motion and classification using advanced radar sensors alone and in combination with image data acquired at visible, near infrared and infrared wave- lengths and with passive microwave data.

Several strategies for synthesis of multiparameter data sets are being pur- sued. For example, multisensor classifications which have included radar images as adjunct bandpasses to optical or infrared sensor systems (Blom and Daily, 1982; Rebillard and Evans, 1983; Evans, 1988; Paris and Kwong, 1988 ) have reported improved classification accuracies over visible and infrared data alone. These studies, however, did not exploit the full polarimetric diversity of current SAR sensors. Evans et al. (1990) described a technique for regis- tering polarimetric SAR data to other data sets which retains data in the com- pressed Stokes matrix format (Fig. 3). In addition, another approach being investigated is to derive geophysical parameters from sensor systems inde- pendently, and to do a combined interpretation on the derived geophysical products (e.g. Fig. 4 and Srinivasan and Richards, 1990).

Change detection techniques are also being investigated to assess their ap-

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plicability to monitoring changes in such key biogeophysical parameters as soil moisture, vegetation state (e.g. Way et al., 1990), and snow moisture and glacier mass balance (e.g. Rott, 1990). Techniques currently under investi- gation are conventional image ratioing (Fig. 5 ), segmented image differenc- ing (described below), and interferometric processing (described in Section 4).

3.2 Image segmentation

An important step toward deriving quantitative geophysical products from radar data is to be able to segment multiparameter images into unique classes, either for unit characterization or for identification of homogeneous areas for further analysis. Segmenting images into classes with similar geophysical characteristics, for example, may allow model inversion to be performed on classes rather than on individual pixels. Segmented images may also be used as input to change detection algorithms when calibrated images are not available.

SEPTEMBER 8, 1989

Fig. 5. Example of multitemporal image ratioing for a soil moisture study area Fresno, CA. Grey field (left arrow) showed no apparent difference in moisture between two days in this CVV image. Dark field [ right arrow ] was drier on second day. (Courtesy of Engman and van Zyl. )

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Burnette et al. ( 1989 ) evaluated unsupervised classification of twenty-two polarimetric parameters for segmenting polarimetric SAR images over an unvegetated target and found that scattering matrix cross terms provided the best discrimination between geologic units. Van Zyl ( 1989 ) described an unsupervised classification technique which provides a means to segment images into classes with varying amounts of vegetation cover. The algorithm classifies scatterers into one of three types based on the relationship between the orientation angles and handedness of the transmit polarization and of the received wave for each transmit polarization, resulting in a classified image.

The three scattering classes are single bounce, dihedral reflection, and diffuse scattering (Fig. 6). For single reflection from a slightly rough dielectric surface the incident wave will experience little multiple scatter. A dihedral corner reflector exhibits a double-bounce geometry, resulting in a 180 ° phase shift between HH and VV. For diffuse scattering highly varying HH to VV phase differences exhibit a noise-like character. The orientation angle of the average scattered wave tracks that of the transmitted wave, a behavior similar to that observed for the single-bounce case. However, the handedness of the scattered wave is the same as that of the transmitted wave polarization which is more consistent with a double-bounce mechanism, van Zyl (1989) and Evans et al. ( 1988 ), noted that this behavior is generated by a class of "three- layer" vegetation models such as the one discussed by van Zyl ( 1985 ), Rich- ards et al. (1987), Ulaby et al. (1988) and Durden et al. (1989). This technique was used by Evans et al. (1988) to map clear-cut areas in a forested area in Maine that were not discernable in L-band HH and VV images. It was also used by Evans and van Zyl (1990) to differentiate areas that had recently been burned near Mt. Shasta in California (Fig. 7 ), and by Evans et al. ( 1988 ) and Evans and Smith ( 1991 ) to differentiate areas with < 10% vegetation cover in a semi-arid region in Wyoming (Fig. 8 ).

Examples of how these segmentation techniques can be used in an automated geophysical processor for derivation of surface roughness, or for monitoring surficial changes, are shown in Figs. 9 and 10, respectively.

3.3 Model inversion

The new capabilities to measure in detail the polarimetric properties of terrain has accelerated interest in modeling of polarimetric behavior based on knowledge of the surface. This modeling effort permits comparison of measured and predicted properties, thus providing the means to interpret re- motely-sensed data in ways not possible before radar polarimetry. Many of the newer modeling approaches require extension of previous models to include calculation of both the amplitude and phase of the various cross-products of the scattering matrix elements that make up the Stokes or covariance matrix elements rather than simply the power measured by various antenna


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combinations. To fully model polarimetric scattering all the cross-products o f the scattering matrix elements, i.e. the full Stokes or covariance matrix must be calculated. Van Zyl et al., ( 1987 ) extended Rice's small perturbation model to the full polarimetric case, retaining scattered fields up to second order. It was shown that this model successfully predicts observed polarization signatures for scattering by the ocean surface and relatively smooth lava fields at

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CURRENT STATUS AND FUTURE DEVELOPMENTS IN RADAR REMOTE SENSlNG 93

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L-band, including the pedestal height of the measured signatures. The model predicts that the height of the observed pedestal increases as the surface roughness increases.

More recently, Van Zyl et al. ( 1991 ) showed that surface microtopography could be inferred directly from multifrequency SAR data by inversion of the small perturbation model. In this study, physical properties at three different 10 m by 10 m sites representing surfaces with rms heights varying from less than one centimeter to tens of centimeters were measured. Ground measurements included soil moisture content, L-band dielectric constant, and heli- copter photography using twin metric framing cameras. The stereo photo- graphs were later reduced to microtopographic profiles and maps, allowing estimation of the power spectrum, rms height and correlation length of the surfaces (Wall et al., 1991 ). AIRSAR data acquired at three different inci- dence angles over the test site were calibrated using trihedral corner reflectors deployed in the area prior to imaging to provide cr ° values for each resolution element in the scenes (Van Zyl, 1990). The modified small perturbation model was then used to infer the power spectra of surface microtopography for the three surfaces and compared to the field measurements. Van Zyl et al. ( 1991 ) found a close match between the estimated and measured quantities, with some over-estimation of roughness for the smoothest surface, probably caused by system noise or penetration and volume scattering.

The results by van Zyl et al. ( 1991 ) allows the derivation of surface rough-

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ness for unvegetated areas through a geophysical processing scheme shown in Fig. 9 (e.g. Evans et al., 1992). For vegetated areas an additional data set [e.g. Thematic Mapper (TM) or High Resolution Imaging Spectrometer (HIRIS) data ] would be required to model the surface.

3.4 Supporting aircraft campaigns

Experience with the ASF GPS has shown that field experiments are critical to algorithm development and validation (e.g. Holt et al., 1990). Multisensor experiments undertaken in support of algorithm development for sea ice studies have the Special Sensor Microwave Instrument (SSMI) validation program (Cavalieri et al., 1991 ) included and Labrador Ice Margin Experi- ment (LIMEX) (Carsey et al., 1989 ). Field campaigns are being undertaken over well-characterized test sites in support of geophysical algorithm development for other disciplines as well. For example, data from the Geologic Remote Sensing Field Experiment (GRSFE) which took place in July and September 1989 will support algorithm development for extraction of surface


TABLE 3

GRSFE aircraft sensor characteristics

95

Airborne Advanced Airborne Thermal Aircraft Visible and Solid-State Terrain Laser Infrared Synthetic Infrared Array Spectro- Altimeter Multispectral Aperture Imaging radiometer System Scanner Radar Spectrometer (ASAS) (ATLAS) (TIMS) (AIRSAR) (AVIRIS)

Wavelength 0.41-2.45/1m 0.47-0.87/1m 1.06#m 8.2-11.7/tm 5.7,24, 68 cm channels 224 29 1 6 3

Swath width (km) 10 2 N/A 12 10

Pixel size (m) 20 4 1-20 m 20 10

Nominal altitude (km) 20 5 1-10 km 8 10

Platform ER-2 C-130 T-39 Sabre Liner C-130 DC-8

property information from remote sensing data of Earth, Mars and Venus (Arvidson and Evans, 1989). Aircraft data sets acquired with prototype sensors for EOS, Mars Observer and Magellan as part of GRSFE include the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), Ad- vanced Solid-state Array Spectroradiometer (ASAS), Airborne Terrain Laser Altimeter System (ATLAS), Thermal Infrared Multispectral Scanner (TIMS) and AIRSAR (sensor descriptions are presented as Table 3 ). These aircraft data and supporting field and laboratory measurements are distributed on a series of 9 CD-ROMs by the NASA Planetary Data System (PDS).

Multisensor and multitemporal data were acquired in support of Hydrol- ogy studies as part of HAPEX-MOBILHY and in the summer of 1988 in the Konza Prairie as part of the First International Land-Surface Climatology Project (ISLSCP) Field Experiment (FIFE) (e.g. Becker et al., 1988). Ad- ditional multisensor airborne campaigns were undertaken in the Summer of 1990 in both humid (Mahantango Creek, Pennsylvania ) and semi-arid (Wal- nut Gulch, Arizona) watersheds; and in 1991 during the European aircraft campaign at Slapton Wood, UK; Orgeval, France; Montespertoli, Italy; La Mancha, Spain, and Oetztal, Austria.

There are also several examples of multisensor and multitemporal experiments in support of ecology studies, including the Forest Ecosystems Dynam- ics (FED) Project in Howland Forest, Maine (Ramson and Sun, 1991 ); the Alaskan Aircraft SAR Experiment (Way et al., 1990); Multiple Airborne Ex- periments Toward Radar Observations (MAESTRO-l) and the Oregon Transect Ecosystem Research (OTER) Project. Additional multisensor and

96 l).k. EVANS

multitemporal experiments are planned tbr 1994 as part of the Boreal Ecosys- tems-Atmosphere Study (BOREAS) (Sellars et al., 1991 ).

4. HIGH RESOLUTION TOPOGRAPHY

An additional data type, topography, is required to complete the three-dimensional view of surface properties and correct for distortions inherent in SAR images. Active microwave techniques using interferometry may provide a feasible method for acquiring these data. Zebker and Goldstein (1986) showed the feasibility of generating topographic databases through radar interferometry. Mission studies and aircraft prototype development are under- way to investigate a variety of methods for acquisition of a global topographic data base (e.g. Cumming et al., 1990; Goldstein et al., 1988; Zebker et al., 1992).

Both NASA/JPL and the Canadian Centre tbr Remote Sensing (CCRS) have implemented aircraft interferometers similar to the one described by Zebker and Goldstein ( 1986 ). For the JPL system, the resulting phase difference image from combining the two complex data sets has been used to gen- erate topographic data with rms error for a l 0 m by l0 m pixel of approxi- mately 2 meters. These data are acquired in a single polarization state at C- band simultaneously with standard L- and P-band polarimeter data so that data can be automatically registered.

In order to address the global-scale problem, generation of a high-resolution DEM of the entire globe that could serve as a calibration data set for all remote sensors is currently being discussed. Among possible options for spaceborne interferometry is the use of subsequent passes of the EOS SAR, or two antennas separated on a single structure or by a tether on a dedicated spacecraft. Once baseline topography is determined, a third interferometric pass can be used to determine what, if any, topographic change has occurred in the intervening time between SAR overpasses (Gabriel et al., 1989 ). Near- cancellation of systematic errors is possible and sensitivity to topographic change on the order of centimeters is attainable. The extreme sensitivity of this technique to elevation changes, high spatial resolution (typically 10 m ), and broad swath coverage means it could be used to monitor changes in surfaces such as soil moisture changes (Gabriel et al., 1989 ). It can also be used to make extensive, accurate measurements of geophysical phenomena, including warping and buckling in fault zones, plate motions, residual displace- ments from seismic events; and motion from changes in surface topography due to eruptions (either explosive activity or events that produce lava flows ), and to swelling of the flanks of a volcano due to the intrusion of magma at a shallow depth.

CURRENT STATUS AND FUTURE DEVELOPMENTS 1N RADAR REMOTE SENSING 97

5. CONCLUSIONS

This brief overview has touched on some of the recent advances in remote sensing using imaging radar sensors. It clearly does not present every recent study, but an at tempt was made to provide examples representative o f key areas. Some areas where there are major challenges in the future include development o f strategies to extrapolate from regional to global scales (i.e., validation of geophysical products, reconciliation between scales of radar backscatter models and atmospheric general circulation models ( G C M s ) ) ; and development of new technology which will result in additional sensor capabilities (e.g. 35 and 90 GHz systems, light-weight electronics, etc. ). With these advances will come many new applications of active microwave remote sensing (e.g. precipitation mapping, subsurface mapping) which in turn will require new techniques for data processing and analysis.

ACKNOWLEDGEMENTS

This work was performed at the Jet Propulsion Laboratory, California In- stitute of Technology, sponsored by the National Aeronautics and Space Administrat ion. I would like to thank Ben Holt and Tom Farr for their helpful reviews of an earlier draft of this paper, and Jakob van Zyl and Ted Engman for providing some of the images used here.

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current status and future developments in radar remote sensing

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