The Alps are prone to a variety of slope deformations. Precise information on the surface displacement of these phenomena is important to analyse and interpret the associated hazard potential. Satellite SAR interferometry is an attractive technology for monitoring surface deformation in large areas, and is now entering an advanced operational phase triggered by the increasing availability of satellite data, in particular Sentinel-1. However, C-band sensors have limited performance in vegetated areas and for comparatively large movement rates (e.g., a few cm/year). In these cases, L-band satellites can complement the high-frequency systems. Here we evaluate the performance of the Argentinian L-band SAOCOM mission for monitoring slope deformation in Alpine regions using standard differential interferograms and time series analysis. Despite the limited number of SAOCOM acquisitions to date and the much larger orbital tube of SAOCOM compared to other current SAR satellite missions, we were able to demonstrate the excellent performance of the Argentinian L-band mission in three different cases. With regular acquisitions every 16 to 32 days and the open distribution policy, SAOCOM can soon be established as an operational and efficient tool for the systematic monitoring of surface movements in Alpine regions, complementing the nationwide maps of land deformation developed based on the regular acquisitions of Sentinel-1 data since 2014.

The Albula Region in the canton Grisons in Switzerland is prone to a large number of landslides. In order to spatially estimate the surface deformation of these phenomena, we performed a multi-temporal interferometric analysis on point (i.e., persistent scatterers) phases by jointly exploiting ALOS-2 PALSAR-2 ScanSAR and StripMap data acquired along the same orbit. Our results indicate a widespread presence of large-scale rock slope instabilities with linear LOS displacement rates in the range of a few cm/year. In comparison with a PSI Sentinel-1 analysis, valid information is retrieved with L-Band also over forests, where only very limited information is available at C-Band. Over built-up regions and for alpine areas above the tree line, the density of points is higher from Sentinel-1 than from ALOS-2 PALSAR-2 because of the moderate resolution of the ALOS-2 PALSAR-2 ScanSAR data.

The interferometric coherence of repeat-pass C-band synthetic aperture radar observations has shown potential for mapping forest above-ground biomass (AGB) when images were acquired with short temporal repeat intervals and under stable environmental conditions. Since 2014, Sentinel-1 collects repeated interferometric image pairs at global scale with repeat intervals of 6 or 12 days. For a semiarid forest site in California, we evaluated one year of Sentinel-1 6- and 12-day (60 and 59 images, respectively) repeat-pass coherence images with the objective of retrieving AGB. The retrieval was performed by inverting the semiempirical Interferometric Water Cloud Model. The retrieval based on individual coherence images was most accurate (~50% relative root-mean-square difference with respect to a Lidar-derived map) when images were acquired in extended periods of dry conditions. The retrieval accuracy improved by approximately 15% units when combining multiple coherence images in the retrieval. Finally, the accuracy of the retrieval with multitemporal 6- or 12-day repeat-pass coherence images differed by only 2% units. The results of this study advocate consideration of Sentinel-1 repeat-pass coherence in AGB mapping efforts in semiarid forest areas.

WBSCAT is a polarimetric scatterometer developed for the European Space Agency for measuring microwave signatures of snow in support of the SnowLab and SnowLab-NG projects. SnowLab-NG includes feasibility studies for ESA candidate satellite missions operating at L-Band such as ROSE-L and Hydroterra. The WBSCAT instrument is based on a vector network analyzer to cover the 1–40 GHz frequency range, including L-Band. Using ground-based data collected in the near-field to model data collected by satellites is challenging when there is volume scattering from a thick layer such as snowpack. WBSCAT has been constructed to use near-field aperture synthesis to permit focusing of backscatter data. Focusing mitigates the effects of near-field operation by synthesizing a narrow antenna beam that illuminates a smaller volume of the snowpack. Using aperture synthesis, an improvement in azimuth resolution by a factor of 6 is possible with WBSCAT at L-band. Initial results from phase calibration of a calibration target response show the potential for this approach.

C-band scatterometer data are among the longest time record of spaceborne observations, dating back to 1991. Their use in the context of terrestrial carbon cycle studies has not been investigated yet. In spite of a weak sensitivity of the C-band backscatter to forest above-ground biomass (AGB), repeated observations have been shown to support the retrieval of biomass. In this paper, we investigate the use of C-band backscatter data at 25 km spatial resolution from the ERS WindScat and MetOp ASCAT sensors arranged in daily mosaics to estimate forest AGB. A retrieval approach based on self-calibration and regression between SAR backscatter and canopy density was developed, producing daily estimates of AGB that were found to well reproduce the global spatial patterns of AGB. A weighted average of the daily AGB estimates was then applied to generate annual maps of AGB. The evaluation of the AGB dataset with recent global datasets of AGB for 2010 confirms the overall reliability of our estimates.

This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3along with a change of S-1 coherence from 0.61 to 0.53.

WBSCAT is a new terrestrial 1-40 GHz polarimetric scatterometer. This instrument, built for the European Space Agency, with additional support from ETH WSL, is currently an element of the ESA SnowLab project for continuous microwave measurements of snowpack in Davos-Laret Switzerland. WBSCAT is based on a compact Vector Network Analyzer (VNA), combined with calibration standards and low-noise amplifiers to increase sensitivity. A pan/tilt positioner provides the angular and spatial diversity required for measurement of radar cross-section, 3D tomographic imaging, and measurements of interferometric coherence. The instrument can apply angular diversity and aperture synthesis to increase radiometric accuracy and suppress clutter. In Davos-Laret, WBSCAT is suspended from a 2.2-meter linear rail positioner that provides additional spatial diversity for high-resolution 3D tomographic imaging.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

The Calaveras fault passes directly through Coyote Dam located near Gilroy, California. The earthen structure of the dam was constructed to withstand the expected deformation due to fault creep at a rate of 10 to 15 mm/year. As part of a possible dam retrofit, the Santa Clara Valley Water District initiated a series of measurements using a Ku-Band terrestrial interferometer to accurately localize the fault trace through the dam. Measurements over the period 12-May 2016 to 18-Nov-2016 were acquired from 4 different positions situated around the down-stream face. Time series of measurements from each position were obtained after performing corrections for variable tropospheric phase delay. These measurements were combined using least-squares estimation to generate three-dimensional maps delineating both stable and rapidly deforming regions.

An acknowledgement provided by the authors was lost during production for the above paper[1]. It is provided here as follows:

SAR tomography can be used as an add-on to persistent scatterer interferometry (PSI) to increase deformation sampling in urban areas by resolving the frequently occurring layovers that are by definition rejected in the PSI processing. This paper, while focusing on the case of a typical high-rise building in layover, quantitatively assesses the potential gain in deformation sampling achieved by the added use of an advanced SAR tomographic technique relative to a PSI approach. At the same time, the quantity of the detected scatterers is weighed against their quality, as assessed on the basis of root-mean-square (RMS) phase deviation between the measurements and the model fit. The quality of the scatterers is also compared with the quality of the persistent scatterers as identified with a PSI approach. The experiments are performed on an interferometric stack of 50 TerraSAR-X stripmap mode images.

Sentinel-1 IWS interferometric time-series analysis (SBAS and PSI) is discussed and results over Mexico City, a mega-city subject to very substantial ground deformation, are presented. In the early steps the processing needs to be adapted to the organization of the data in sub-swaths and burst, and for the SLC co-registration an extremely accurate co-registration is required for interferometry, because of the strong along-track Doppler centroid variation. Furthermore, a deramping of the co-registered SLC for an along-track phase ramp present in each burst is applied. After that interferometric time-series techniques (SBAS, PSI) can be applied in the same way as for stripmap mode data.

The GPRI2 Ground-Based Real-Aperture radar was deployed in Seattle over a 6-week period during March and April 2015 to measure sub-millimeter scale subsidence related to construction of the Alaskan Way Viaduct and Seawall Replacement Program (AWVSRP). The radar was located on the mid-level roof of the Smith Tower overlooking Pioneer Square at an altitude of about 75 meters above street level. Radar images at 2 hour intervals were processed to obtain Line-of-Sight (LOS) deformation maps. Point target analysis was used to identify stable targets and to mitigate variations in propagation path length due to atmospheric changes. Geolocation and rectification of the more than 100,000 monitored points was carried out using a LiDAR DEM.

Persistent scatterer interferometry (PSI) is in operational use for spaceborne synthetic aperture radar (SAR)-based deformation analysis. A limitation inherently associated with PSI is that, by definition, a persistent scatterer (PS) is a single dominant scatterer. Therefore, pixels containing signal contributions from multiple scatterers, as in the case of a layover, are typically rejected in the PSI processing, which in turn limits deformation retrieval. SAR tomography has the ability to resolve layovers. This paper investigates the added value that can be achieved by operationally combining SAR tomography with a PSI approach toward the objective of improving deformation sampling in layover-affected urban areas. Different tomographic phase models are implemented and compared as regards their suitability in resolving layovers. Single-look beamforming-based tomographic inversion and a generalized likelihood ratio test (GLRT)-based detection strategy are used to detect single and double scatterers. The quantity of the detected scatterers is weighed against their quality as defined in terms of the phase deviation between the single-look complex (SLC) measurements and the tomographic model fit. The gain in deformation sampling that can be derived with tomography relative to a PSI-based analysis is quantitatively assessed, and alongside the quality of the scatterers obtained with tomography is compared with the quality of the PSs identified with a PSI approach. The experiments are performed on an interferometric stack of 50 TerraSAR-X stripmap images. The results obtained show that, although there is a tradeoff between the quantity and the quality of the detected scatterers, the tested SAR tomography approach leads to an improvement in deformation sampling in layover-affected areas.

SAOCOM is an Argentinian L band system formed by two satellites (SAOCOM-1A and SAOCOM-1B). ESA is investigating the possible applications of a companion satellite (SAOCOM-CS) carrying a passive receiver working in concert with one of the SAOCOM-1 satellites. During the mission there will be cycles with a long along-track bistatic baseline, suitable for deformation monitoring. Together with the combination of ascending and descending orbits, this geometry will produce four measurements from different viewing geometries, enabling us to estimate the 3D motion vector. Here we investigate the sensitivity of such a configuration, and the opportunities for increasing the density of persistent scatterers.

Dry snow is conventionally considered as having minimal effect on microwave radiation at long wavelengths (such as L-band). However, dry snow affects observed microwave signatures even at these wavelengths through changes in impedance matching between soil and the overlying media, as well as through changes in the refraction angle at the soil interface. Exploiting these effects, the multi-angular, dual-polarized L-band observations of e.g. the European Space Agency's SMOS (Soil Moisture and Ocean Salinity) mission have the potential to derive snow properties, such as the density of the lowest layers of the snowpack in contact with the ground. This in turn, would have the potential to inform retrieval schemes of snow cover based on EO-data from other sensors. In addition, the theoretical studies demonstrate that the effect of dry snow on retrieval of other geophysical variables, such as soil moisture, is not negligible. In this study, we demonstrate the simultaneous retrieval of snow density and ground permittivity in dry snow conditions, using a multi-year dataset of tower-based L-band observations. We show that following predictions of the theoretical studies, the retrieved snow density matches that of the density measured for the lowest snow layers; dry snow cover is also shown to affect retrievals of ground permittivity by up to 40 %.

Persistent scatterer interferometry (PSI) assumes the presence of a single temporally coherent scatterer in a range-azimuth pixel. Multiple scatterers interfering in the same pixel, as for the case of a layover, are typically rejected. Conventional SAR tomography (3D SAR) is a means to separate the individual scatterers in layover. Advanced tomographic inversion approaches employing extended phase models additionally allow simultaneous retrieval of scatterer elevation and deformation parameters. In this way, SAR tomography can increase deformation sampling and thereby complement a PSI-based analysis. This paper investigates the use of tomography as an add-on to PSI for spatio-temporal inversion of single and double scatterers in urban areas. Results are provided on an interferometric stack of 50 stripmap TerraSAR-X images acquired over the city of Barcelona.

SAR tomography as an extension to persistent scatterer interferometry (PSI) approaches has the potential to improve the level of detail of retrievable information, in particular, in the case of layover scenarios in urban areas. In this paper, a processing approach is sketched that eventually allows for retrieving a 3-D point cloud of point-like scatterers based on subsequent PSI and SAR tomography processing of an interferometric stack of high-resolution spaceborne TerraSAR-X data acquired over the city of Barcelona between the years 2008 and 2012. Experimental results are presented in the form of (1) vertical tomographic slices of high-rise buildings and (2) a 3-D point cloud of a larger district of the city of Barcelona retrieved from the tomograms.

Agile SAR platforms such as an automobile require a flexible SAR processing scheme to account for nonlinear sensor trajectories during the synthetic aperture. In this contribution, a parallelized implementation of a time-domain back-projection SAR focusing algorithm based on NVIDIA's CUDA GPU computing framework is presented and discussed using a car-borne SAR data set. The processing performance is assessed using different hardware. In addition, a pre-processing scheme is described that allows for full 3-D motion compensation, yet staying conveniently in conventional slant-range/azimuth geometry of single-look complex SAR images.

The paper presents advances in regional mapping of forest growing stock volume (GSV, unit: m3/ha) using multitemporal ALOS PALSAR images. For this, an approach based on the Water Cloud Model has been used. Fine Beam Dual (FBD) and Wide Beam (WB) mode backscatter data acquired during 2010 were inverted to GSV; the individual GSV estimates were then combined to form an improved estimate. Examples of regional mapping for the boreal, temperate and tropical zone are here presented. Estimates were obtained at 25 m resolution using FBD data only and at 75 m combining multi-looked FBD estimates and WB estimates. The inclusion of multi-temporal WB data served to improve the spatial characterization of GSV. At pixel level, the discrepancy with respect to other estimates from Earth Observation data was large (relative RMSE ~ 90%). Aggregation to lower resolution (e.g., 1 km) increased the agreement and a relative RMSE mostly below 30% was obtained.

Persistent scatterer interferometry relies on the assumption that only one dominant scatterer is present per range-azimuth resolution cell. If this criterion is not met the point target candidate is discarded during the iterative processing sequence. This one-scatterer assumption contrasts with the fact that in urban scenarios layover is an ubiquitous phenomenon, and, therefore two or even more scatterers per resolution cell occur frequently. SAR tomography has the potential to support persistent scatterer interferometry in urban areas by providing a means to identify and separate two scatterers in elevation direction. In this paper, we explore an interferometric stack consisting of 25 ENVISAT/ASAR SLC images over Bucharest using SAR tomography approaches combined with interferometric point target processing. Elevation profiles are extracted using beamforming and truncated singular value decomposition focusing approaches.

In this contribution, a car-borne SAR and InSAR experiment is described. The slope of a valley was imaged by means of a single-pass InSAR system mounted on a car driving on roads along the bottom of the valley. The GAMMA portable radar interferometer GPRI-II hardware with a modified antenna configuration was used for data acquisition. The experimental setup (1), SAR imagery focused along a slightly curved sensor trajectory (2), and first interferometric results (3) obtained using this configuration are presented.

Precise terrain-corrected georeferencing of synthetic aperture radar (SAR) images and derived products in range–Doppler coordinates is important with respect to several aspects, such as data interpretation, combination with other geodata products, and transformation of, e.g., terrain heights into SAR geometry as used in differential interferometric SAR (DInSAR) applications. For georeferencing, a lookup table is calculated and then refined based on a coregistration of the actual SAR image to a simulated SAR image. The impact of using two different implementations of such a simulator of topography-induced radar brightness, 1) an approach based on angular relationships and 2) a pixel-area-based method, is discussed in this letter. It is found that the pixel-area-based method leads to considerable improvements with regard to the robustness of georeferencing and also with regard to radiometric normalization in layover-affected areas.

In this letter, we report the presence of a localized Doppler anomaly occurring during the focusing of a Radarsat-2 data set acquired on the Merapi volcano (Indonesia) during the devastating 2010 eruption. The Doppler anomaly is manifested as ~ 3-km-wide bull's-eye-shaped azimuth pixel shifts between two subaperture images. The Doppler anomaly is centered on the summit-south flank of the Merapi volcano. The pixel shifts reach up to 11.6 m. Since the Merapi volcano was undergoing a large eruption during the data acquisition, it is possible that there is a volcano-related phenomenon that has delayed the radar signal so much to create measurable pixel offsets within a single synthetic aperture radar (SAR) data set, similar, but more extensive, to the signal generated by targets motions; similar, but less extensive, to the signal generated by ionospheric perturbations. It is known that the SAR signal is delayed as it passes through heterogeneous layers of the atmosphere, but this delay typically affects the SAR signal to a fraction of the phase cycle or few centimeters depending on the radar wavelength employed by the system. We investigate the source of this anomalous metric signal; we review the theoretical basis of SAR image focusing, and we try to provide a consistent physical framework to our observations. Our results are compatible with the SAR signal being perturbed during the actual process of image focusing by the presence of a contrasting medium located approximately between 6- and 12.5-km altitude, which we propose being associated with the presence of volcanic ash plume.

In this contribution Persistent Scatterer Interferometry (PSI) processings done in the framework of the FP7 Project PanGEO over a number of European sites are presented. After a short introduction of the PanGEO project, we present the data used, the processing done and the results of the PSI analysis. The influence of specifics as the presence of large non-urban areas, significant topography, seasonal effects, special deformation characteristics, and the size of the available data stacks on the processing are highlighted.

The city of Venice (Italy) and its surrounding lagoon constitutes a unique worldwide environment vulnerable to loss of surface elevation due to land subsidence and sea level rise. Locally, the city of Venice, shows a present general stability with rates averaging between -1 to +1 mm/year. This work presents results over Venice area, derived by the two interferometric techniques (DInSAR and Permanent Scatterers Interferometry). The use of the two techniques allows us to understand the complementarity of both techniques and their limitations.

In this paper the results of an additional campaign in the context of Flashing Fields are presented showing the impact of soil surface roughness on the directional backscattering. For the characterization of soil surface roughness a photogrametric measurement device was used and roughness was measured simultaneous to SAR observation made by ERS-2 and TerraSAR-X. As this study mainly confirmed and consolidated the findings of the previous Flashing Fields studies, major progress was made in in the understanding of the impact of soil surface roughness on the flashing phenomenon. It could be concluded, that besides the row orientation, certain roughness conditions could significantly alter the flashing effect.

Spaceborne techniques to monitor the Earth and its processes have advanced significantly over the last thirty years. Sensor spatial resolutions have increased supporting more detailed measurements and shorter revisit times have given higher temporal resolutions. Notwithstanding such advances, understanding the Earth system as a whole remains challenging. The focus of this paper is on monitoring rock glaciers in a mountainous area in the municipality of Graechen, Valais, Switzerland using Envisat and TerraSAR-X data acquired in 2008, 2009 and 2010. Spaceborne Synthetic Aperture Radar interferometry (InSAR) is used to augment existing observations from borehole measurements, geodetic and photogrammetric methods. In the Graechen study area, significant phase differences are reliably observed in 11-day TerraSAR-X and 35-day Envisat differential interferograms related to movements of the “Distelhorn” rock glacier and the Ritigraben rock glacier. The number of visible fringes are a relatively clear indication for seasonal variations in the movement of the rock glaciers, being about a quarter of a fringe faster in autumn (September/October) than in summer (June, July).

Multi-temporal synthetic aperture radar (SAR) metrics are investigated to assess their capability for mapping land cover types. The temporal variability and the minimum backscatter in a time series of Envisat Advanced SAR (ASAR) Wide Swath Mode (WSM) backscatter measurements present unique features over water surfaces with respect to other land cover types. A simple thresholding algorithm applied to these metrics is then presented to detect water bodies. Results from six study areas in Europe and Central Siberia show consistency of the behavior of the multi-temporal metrics over “water” and “land” areas regardless of the specific region. Water body detection accuracies are mostly above 90%, omissions occurring when a water body has a size similar to the pixel size of the SAR image.

Arctic and mountainous periglacial areas are subject to intense freezing cycles and characterized by remarkable surface displacement. We used TerraSAR-X interferometry to analyze the seasonal surface displacement on two regions in Alaska and Switzerland. In both cases, TerraSAR-X interferograms with an 11 days time interval are computed in series in order to maximize coherence. Seasonal subsidence maps highlight surface displacements of several centimeters while time-series show non-linear behaviors of the movements.

In the frame of the IGARSS 2012 special session on ENVISAT the landslide mapping activities in Switzerland using ENVISAT ASAR data are presented. Between 2005 and 2010 the building up of a well suited archive over the Swiss Alps was realized through programming of all IS2 mode data during the snow free period. In recent years DINSAR and PSI based landslide inventory and monitoring products started to play an important role in the updating of hazard maps.

Azimuth spectrum band-pass filtering has been applied successfully for estimation of along-track ground displacement [1] as well as for other applications, such as the identification of directional scattering [2] and the coherence estimation for long-baseline pairs [3]. Particularly L-band split-beam interferograms have shown another phase component related to along-track variations in the ionospheric path delay. In our work we present methodologies to identify and quantify ionospheric path delays affecting an interferogram using the corresponding split-beam interferogram.

In this letter, the requirements to derive topography from a portable terrestrial radar interferometer are introduced, the instrument design and the relationship between interferometric phase shift and surface topography are explained, and two examples of topographic maps from measurements at the Rhone glacier and Grabengufer rock glacier in Switzerland are presented. In the first case, an external digital elevation model was used to assess the error of topography mapping with the portable radar interferometer and to analyze ice surface changes of the glacier in the last 14 years. We found that the height error standard deviation is about 3 m within a distance of 2 km from the sensor and observed massive thinning of the Rhone glacier. In the second case, we used the terrestrial radar interferometer in order to measure the height difference between August 2009 and March 2010 over the rock glacier as a consequence of its destabilization.

ERS-ENVISAT cross-interferometry is a unique tool for a number of applications since it combines a short repeat-pass interval (28 minutes) with a long perpendicular baseline (2 km). In the present work a methodology to use the cross-interferogram phase to map the ice thickness of frozen tundra lakes and northern rivers. After presenting the methodology used examples over sites in northern Canada and Siberia are shown. For one site the results could be compared to in-situ measurements and showed good correspondence.

Interferometric observations between the European Remote Sensing, ERS-2, synthetic aperture radar (SAR) and the Envisat Advanced SAR (ASAR) are unique since they are characterized by a short repeat-pass interval (28 min) and a perpendicular baseline of approximately 2 km. In vegetated areas, this configuration should preserve from strong temporal decorrelation and enhance the sensitivity of coherence and SAR interferometric (InSAR) phase to volumes with small heights. This assumption could be tested with the data acquired during the dedicated ERS-Envisat Tandem mission on October 15, 2007, over the Seeland region, Switzerland. Five maize fields and one sunflower field presented lower coherence and offsets of the interferometric phase, i.e. height, with respect to neighboring bare fields. To gain understanding on the interferometric signatures, the interferometric water cloud model was used to simulate coherence and InSAR height for the maize fields. Both the coherence and the InSAR height present clear dependence upon vegetation height and exhibit strong consistency. Simulations showed that the modeled coherence and InSAR height are most sensitive to the two-way attenuation and the temporal coherence of the vegetation. The best correspondence between the observed and modeled InSAR parameters was obtained with two-way attenuation values between 2 and 4 dB/m (corresponding to an extinction between 1 and 2 dB/m) and high temporal coherence of the vegetation (above 0.6), with this being due to the very stable conditions of the weather during the 28-min interval between image acquisitions.

In the past, the application of Persistent Scatterer Interferometry (PSI) was primarily possible in the case of slow (less than a few centimeters per year) uniform movements. In this paper, we show how PSI permits the monitoring of relatively fast (including rates up to > 50 cm/year) and nonuniform movements using TerraSAR-X repeat observations over deep-level mining. To enable this, parts of the PSI methodology were adapted to the special characteristics of the example studied. Apart from a description of the methodology used and the result achieved, error considerations and a validation of the result with in situ measurements are included.

Four TerraSAR-X stripmap mode scenes acquired during the late summer of 2008 in the Oberwallis region in the Swiss Alps have been interferometrically analyzed for the survey of the activity of unstable slopes. Differential interferometry was applied using a high precision external Digital Elevation Model (DEM). Position, extent, contour, and approximate velocity of unstable slopes were determined for a large area. Selected results representing rockglacier movement and landslide activity are discussed and compared with in-situ information and interferograms derived from ENVISAT ASAR, JERS-1 SAR and ALOS PASLSAR data. This application strongly benefits from the higher spatial resolution of the TerraSAR-X data in comparison to the C- and L-band sensors used in the past, because many of the instabilities are of relatively small size.

Before the summer of 2007 58 square Trihedral Corner Reflectors (TCR) were installed in salt marshes within the Venice Lagoon where anthropogenic structures completely lack or few constructions are scattered at a distance from one another too large to reliably resolve the radar phase ambiguity on ERS and ENVISAT interferometric point target analysis. An optimal TCR network has been established taking into account the location of ¿natural¿ point targets in ERS and ENVISAT SAR interferometric analyses and keeping to a value of about 1 km the maximum distance between the TCR or between an ¿artificial¿ and the adjacent ¿natural¿ reflectors. In this contribution we discuss the set-up of the TCR, their backscattering intensity response on ENVISAT ASAR and TerraSAR-X images, and the approach followed to measure the ground displacement in areas of particular interest within the Venice Lagoon.

ERS-ENVISAT cross-interferometry is a unique tool for a number of applications since it combines a short repeat-pass interval (28 minutes) with a long perpendicular baseline (2 km). Temporal decorrelation effects are limited and the sensitivity to topographic features is strongly enhanced. In this contribution the focus is on problems encountered during the coherence estimation in ERS-ENVISAT cross-interferometry. Because of the ERS-2 Doppler Centroid variations the azimuth common band available is often only a relatively small fraction of the PRF. Similarly, in the case of not ideal baselines, the common range bandwidth is often much smaller than the chirp bandwidth. Furthermore, high phase gradients in the cross-interferograms can significantly affect the coherence estimates. In our contribution we propose methodologies to reduce these problems in the coherence estimation.

Retrieval of forest stem volume from synthetic aperture (SAR) backscatter and interferometric SAR (InSAR) coherence is generally performed using a model-based approach, where in situ measurements are necessary to estimate the unknown model parameters. Problems arise when in situ data are either not available or of low quality or the observables present spatial variations. In this work we present three approaches for automatic modeling and inversion of forest backscatter and coherence to retrieve forest stem volume. The three approaches exploit statistical distributions of the observables to obtain estimates for the unknowns in the model. Results shows remarkable agreement with those obtained by means of traditional modeling approaches based on in situ data.

ERS-ENVISAT cross-interferometry is a unique tool for a number of applications since it combines a short repeat-pass interval (28 minutes) with a long perpendicular baseline (2 km). Temporal decorrelation effects are limited and the sensitivity to topographic features is strongly enhanced. The short repeat-pass also allows monitoring of fast-moving displacements. In this paper we show a number of examples on Digital Elevation Map (DEM) generation in flat areas, crop height retrieval and displacement monitoring using data from the recent tandem mission dedicated to ERS-ENVISAT interferometric acquisitions. The examples demonstrate the feasibility of the applications, highlighting at the same time some limitations, which are due primarily to the very specific configuration of the interferometric system.

In our contribution we present an initial assessment of the applicability of TerraSAR-X for repeat-track interferometry. A series of TerraSAR-X repeat observation in stripmap mode over an test site that includes different land use classes as well as fast and slow deformation phenomena was selected for our investigation. From our investigation we conclude that long-term interferograms can mainly be interpreted over urban areas. For forested areas and for denser agricultural vegetation such as fully grown crops the coherence levels are very low even for 11 day intervals. For sparse vegetation as present in late winter the coherence is high, over many fields even over several 11-day cycles. The high spatial resolution is ideal to measure localized deformation patterns. For slow deformation at larger scale confusion with atmospheric effects is strong. Multi-temporal approaches can be used to resolve the deformation and atmospheric phase components if sufficient numbers of observations are available.

Satellite interferometry has been used extensively for ground-motion monitoring with good success. In the case of landslides, for example, space-borne SAR interferometry has a good potential to get an overview on the slope stability. The role of a space-borne INSAR as an element in a landslide or rock fall warning system is constrained by the specific space-borne SAR imaging geometry, the typical multiple-week repeat-interval, and uncertainties in the data availability. Most of these limitations can be overcome with an in-situ radar imaging system. GAMMA has developed a portable radar interferometer that utilizes real-aperture antennas to obtain high azimuth resolution. Images are acquired line by line while rotating the transmitting and receiving antennas about a vertical axis. Phase differences between successive images acquired from the same location are used to determine line-of-sight displacements. The instrument operates at 17.2 GHz and has measurement sensitivity better than 1 mm. The instrument uses two receiving antennas with a short baseline to form an interferometer. Phase differences between simultaneous acquisitions by these antennas are used to calculate the precise look angle relative to the baseline, permitting derivation of the surface topography. Expected statistical noise in the height measurements is on the order of 1 meter. In this contribution the design, measurement principles and characteristics of GAMMA's Portable Radar Interferometer (GPRI) are presented.

Information on snow coverage, structure, and liquid water content are important for many applications including avalanche warning, numerical weather prognosis (NWP), and snow pack water storage estimate. Active microwave remote sensing from space has an excellent potential to address these needs. However, the current generation of C-band SAR satellite systems are not well suited for snow related applications because of the small impact of the dry snow cover on the backscattering signal at 5.3 GHz. In order to be more sensitive to snow properties the envisaged ESA CoReH2O mission proposes a dual-frequency radar operating at 9 and 18 GHz (X- and Ku-band). In support of this mission, a ground based 9 to 18 GHz scatterometer is being developed to help to address the lack of simultaneous backscatter information of snow in this frequency range. We are also preparing a dedicated field campaign in the Swiss Alps to evaluate the performance of the system and acquire first data.

L-band (1-2 GHz) microwave radiometry is an important remote sensing technique to monitor soil moisture over land surfaces at the global scale. The ESA L-Band SMOS radiometer mission aims at providing asglobal maps of soil moisture, with accuracy better than 0.04 m3m-3every 3 days, with a spatial resolution better than 50 km. To improve the models used and for specifying the accuracy of the aimed global soil moisture data product, ground based radiometer campaigns before launch, during the commissioning phase and during the operative SMOS mission are important. Furthermore, the availability of ground-based L-band data measured simultaneously with the over-flying SMOS satellite are required for calibration and validation purposes. To address these needs three ELBARA II radiometers are being constructed for ESA. They rely on the proven architecture of the ELBARA radiometer [1] with improvements on the user interface, the mechanics, and the microwave electronics. Especially the development of an Active Cold Load (ACL) [2] as cold reference is expected to improve the radiometric accuracy.

The objective of this paper is to carry out a systematic investigation about the sensitivity of radar to maize crop growth and soil moisture by considering a wide range of frequencies and angles and all linear polarizations. We show the results of a correlation study carried out on the data collected on a maize field at Suberg, in the Swiss region named Central Plain, by the multifrequency RAdio ScAtteroMeter (RASAM). This agricultural field was monitored over a long period of time at a wide range of frequencies and observation angles so that the correlation between the backscattering and crop height and the biomass and soil moisture was studied under several plant and observation conditions. Moreover, we describe some recent refinements applied to the vegetation scattering model developed at Tor Vergata University, Rome, Italy, and we evaluate the accuracy of extended comparisons between model outputs and RASAM signatures. The Tor Vergata model is finally applied to give a theoretical basis to the experimental correlation findings.

The accuracy of SAR interferometric coherence estimates depends on the precision of several processing steps. In particular decorrelation can occur if range common-band filtering does not perform optimally. Typically a planar surface is adopted which introduces additional decorrelation in case of sloped terrain. To take into account topographic variations a slope-adaptive range common-band filtering method has been developed. A DEM is used to simulate an unwrapped interferogram and the fringe rate is used as driver for the filter size in the range common-band filtering step. Tests with several spaceborne interferometric SAR datasets confirmed the robustness of the method. The improvement of the coherence increased for increasing perpendicular baseline. As a consequence, the fringe visibility also greatly improved. To quantify the improvement of the coherence estimates with the slope-adaptive range common-band filtering, we considered the variation of classification in forest mapping, i.e. an application in which accurate coherence estimates are needed. With improved coherence the classified forest stem volume agreed better with forest maps derived from other remote sensing datasets.

The goal of this contribution is the assessment of the potential of a C-band SAR mission with repeat-pass interval of 12 days - as the intended European satellite system Sentinel-1 - to derive ice surface velocity using SAR interferometry (InSAR) and offset-tracking. For this purpose we investigated ERS-1 SAR data acquired during the ice missions in 1992 and 1994 in 3-day repeatorbits at Nordaustlandet in the northeast Svalbard archipelago. In 12-days winter InSAR pairs phase decorrellation is mainly observed in areas of high strain rates and, in certain cases, because of snow melting or redistribution through snowfall or wind. Velocity maps derived from these image pairs were found useful to enhance in regions of slow glacier flow the mapping of the surface ice-flow divides previously determined from optical imagery and topographic information. Range-azimuth offset-tracking investigations suggest that the expected error of this method is on the order of 50 m/year and that spatial coverage is generally satisfactory. Application of dual-azimuth offsettracking, use of HH polarization and enhanced spatial resolution, feasible with Sentinel-1, could enhance the expected error.

In this work, we show the correlation study carried out on the data collected on a maize field in the Swiss region named Central Plain, by the multifrequency RASAM scatterometer. This agricultural field was monitored over long periods, at a wide range of frequencies and observation angles, so that the correlation between backscattering and crop height, biomass and soil moisture could have been studied under several plant and observation conditions. Moreover, we describe some recent refinements applied to the vegetation scattering model developed at Tor Vergata University, and we evaluate the accuracy of extended comparisons between model outputs and RASAM signatures. The Tor Vergata model is finally applied to give a theoretical basis to the experimental correlation findings.

In the alpine zone snow is a dominant factor for more than half of the year and has strong influence on the ecosystem and economy. The knowledge of snow coverage, structure, liquid water content etc. is important and useful for many applications ranging from flood management to avalanche warning. Remote sensing from space has good potential to address these needs. Within ASSIST, Alpine Safety, Security and Information services and Technologies, these topics are also of interest. Two snow related products were identified that can be produced on an operational base with the available satellite systems to be ingested into the ASSIST service. Avalanche maps, mapping the contours of avalanches, and snow cover maps, mapping the snow covered area. The produced products are in good agreement with validation data. Unfortunately the current available satellite systems (mainly c-band SAR that is applicable) are not very well suited for snow related applications due to the small influence of the dry snow on the microwave signal at C- band. To overcome this limitation the CoReH2O mission was designed. With its X- and Ku-band system and repeat rates of 3 and 15 days it has high potential for alpine snow applications. Additional microwave signature measurements at these frequencies with standardized and reproducible snow characterization information will be needed for model development and validation. Recent developments allow a more quantitative snow characterizations and will be considered in the SnowScat project in combination with traditional snow characterization methods.

European Remote Sensing satellite (ERS) ENVISAT coherence is a new repeat-pass interferometric synthetic aperture radar product characterized by a very short repeat-pass interval between acquisitions (28min). In this paper, we investigate the properties of two 28-mins coherence images acquired over the regions of Paris, France, and Prague, Czech Republic, as well as a 35-day coherence image from Prague. Bare soils showed higher coherence compared to forests and urban areas. The range of coherence was larger for 28-min pairs, perpendicular baseline closer to 2km, and stable weather conditions between acquisitions. Coherence modeling shows that because of the long baseline: 1) significant surface decorrelation occurs when slopes are not accounted for in the common-band filtering and for variation of topography in the resolution cell and 2) volume decorrelation is significant. Temporal decorrelation seems to be relevant for vegetation even at the 28-min interval time scale. Observations and modeled coherence were found to be in good agreement. Land cover classification using coherence and backscatter has been tested for the Paris scene for mapping the four major classes. The classification accuracy was 86% when an edge-eroded version of the reference land cover map was used. It decreases to 68% when the full land cover map was used as reference because of local mismatches between the coherence image and the land cover map, and the reduced common band in range. The high coherence difference between forests and bare fields suggests the possibility to use the ERS-ENVISAT coherence for forest/nonforest mapping and estimation of biophysical properties of short vegetation. Coherence, decorrelation, ENVISAT Advanced Synthetic Aperture Radar (ASAR), European Remote Sensing satellite (ERS), land cover, synthetic aperture radar interferometry (InSAR).

ERS-ENVISAT coherence is a new SAR interferometric product with interesting properties due to the very short repeat-pass interval between acquisitions. When the requirements set by ERS-ENVISAT interferometry on baseline length and Doppler centroids are fulfilled, the ERS-ENVISAT coherence can present a large span of values. For an ERS- 2/ENVISAT image pair acquired over the region surrounding the city of Paris, bare soils have much higher coherence compared to forests and urban areas. As a consequence the use of the ERS- ENVISAT coherence for detecting forest cover changes and estimation of biophysical properties of regrowing forest / crops seems feasible. An analysis of the decorrelation phenomena for the ERS-ENVISAT coherence shows that (i) decorrelation can increase from near to far range if the common band filtering is not perfect, (ii) volume decorrelation is relevant because of the long baseline, and (iii) temporal decorrelation seems to be relevant for vegetation even at the 30 minutes interval time scale.

An analysis of the terrain displacement along a funicular in Switzerland was performed by satellite synthetic aperture radar interferometry using ERS-1/2 data. An initial interferometric point target analysis was performed with images acquired before 1999 and excluding all winter acquisitions with snow cover. The line-of-sight profile of the displacement rate along the funicular shows maximum values of about 1.5 cm/year, with negligible displacements near the lower and upper stations. After the spring of 1999, when displacements of several decimeters occurred because of heavy rainfall, the analysis was continued with single interferograms on the previously identified point targets. With a series of interferograms with large baselines the displacement could be analyzed in 1999 and 2000 along parts of the funicular.

This paper assess the possibilities of the synthetic aperture radar (SAR) sensors currently in orbit for the maize monitoring defining the configurations (polarization and incidence angles at C-band) maximizing the sensitivity to plant growth and reducing the impact of the soil moisture on the signal. Temporal evolution of the signal was simulated in all the possible configurations using the radiative transfer model developed by the University of Rome "Tor Vergata." The input parameters came from an intensive field campaign providing a detailed description of maize crop over the Belgian Loamy site all along the 2003 growing season. The model was validated for vertical (VV) and horizontal (HH) polarization using ERS, ENVISAT, and RADARSAT observations. The C-band SAR signal in single polarization was found to be sensitive to crop growth till the leaf area index (LAI) reached 4.6 m/sup 2//m/sup 2/, while the soil moisture influenced the signal for sparsely vegetated fields (LAI<2.7 m/sup 2//m/sup 2/). Dual-polarizations indexes were found sensitive to maize growth and less sensitive to soil moisture variations. The VV/VH polarization ratios computed from signal recorded at high incidence angle (35/spl deg/ to 45/spl deg/) could be considered to assess the crop growth till LAI reached 4.9 m/sup 2//m/sup 2/ with low sensitivity to soil moisture. At the beginning of growth, the emergence of maize plants could be detected using the copolarized ratio (VV/HH) computed at low incidence angle. These indexes allow discriminating various crop conditions at a given date between fields of a same region.

Several efforts, aimed at developing and refining crop backscattering models, have been done during the last years. Although important advances have been achieved, it is recognized that further work is required, both in the electromagnetic characterization of single scatterers and in the combination of contributions. This work is focused on the description of leaf geometry and of the internal structure of stems. Recently developed routines, able to model the scattering cross sections of curved sheets and hollow cylinders, are adopted for this purpose and run within the multiple-scattering model developed at the University of Rome "Tor Vergata". Input parameters are taken from experimental campaigns. In particular, ground data collected over a maize field at the Central Plain site in 1988, over wheat and maize fields at the Loamy site in 2003, and over wheat fields at the Matera site in 2001 and 2003 are considered. The multitemporal backscattering coefficients at C-band are simulated. The results obtained under different assumptions are compared to each other, and with C-band radar signatures collected over the same fields. The influence of some critical factors, affecting crop backscattering, is discussed. It is demonstrated that a more detailed scatterer characterization may improve the model accuracy, especially in the case of hollow stems.

Large differences are observed between radar backscatter measurements made by the ENVISAT and ERS-2 satellite synthetic aperture radars, within 30 min of each other over certain agricultural fields in Flevoland, The Netherlands. The differences appear to be caused by the presence of highly directive scattering combined with very small variations in the azimuth illumination angle of the two sensors at the degree or subdegree level. There is relatively little awareness of such sensitivity to illumination direction, and it is not predicted by models for microwave interaction usually applied to soils and vegetation. It is, however, to be expected if there are significant contributions from coherent scattering extending over patches of the agricultural fields in question. We present analysis that supports such a conclusion.

Multi-temporal, multi-incidence ASAR WSM data over Siberia were used to derive land cover information. In an initial step several hundred ASAR WSM scenes were integrated into a database. Then the backscattering characteristics (signatures) of different land cover types were investigated. Based on the results, methodologies to retrieve land cover classes, including different forest biomass classes, were developed and applied. Parameters considered include a temporal backscattering variability parameter and an incidence angle dependence parameter.

Interferometric point target analysis using Gamma Remote Sensing's IPTA module has been applied to two interferometric stacks of RADARSAT-1 FINE mode data over the Belridge/Lost Hills oil fields in California. The stacks (/spl sim/30 images each) are both from descending orbits and span the same 2 year time period (2002/02-2004/02) but incidence angles are different (F1 vs. F3F). An IPTA analysis is carried out on each stack separately and results are then compared in terms of achieved density of the scatterers and residual errors of the motion analysis. We verify the accuracy of our methods indirectly by studying the subtle difference in line-of-sight motion caused by the different incidence angles of the data stacks (38.4 vs. 43.1 degrees at the study site).

There exists a significant demand for the monitoring of mining induced surface deformation for legal obligation safety, and environmental reasons. In recent years land surface deformation monitoring with SAR data reached some maturity. Nevertheless, there remain important limitations to the availability of the EO based information. Reasons include incomplete spatial coverage with information gaps for low coherence areas, problems in resolving high phase gradients, and unavailability of the required SAR data. The objective of our contribution is to show that important improvements to these limitations are still possible. In vegetated areas and for fast deformations L-band INSAR was found to be more robust with a wider applicability than C-band INSAR. In some low coherence areas point-like scatterers can be identified which permit to fill important information gaps. Furthermore, pairs with long baselines can be included in the interferometric analysis, supporting a more complete use of the available SAR acquisitions and to improve the temporal sampling

The UNOSAT service is an ESA Earth Observation Market Development initiative whose objective is to encourage, facilitate, accelerate and expand the use of accurate geo-information derived from EO satellite imagery by professionals involved in the implementation of humanitarian aid and development assistance projects. We focus on the role of SAR within this context. Space-borne SAR has a particularly good potential to support disaster management and humanitarian relief projects thanks to its all-weather capability, its capabilities for change detection, the large existing data archives, and, of course, the geometric and thematic information content of the images. Basic processing techniques, including precision image co-registration, multi-temporal analysis, terrain corrected geocoding, interferometry, and radargrammetry are well developed and operational. Advanced techniques like differential interferometry and interferometric point target analysis (IPTA) became more and more operational during the last years and provide unique information about terrain stability and deformation.

The objective of this paper is to present an approach that combines the interferometric point target analysis (IPTA) and the extended target based interferometric techniques (InSAR). The improvements achieved are most obvious in cases where the individual techniques result in complementary coverage in the information retrieved.

A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis.

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by model simulations and multitemporal ground data measured on fields taken as a reference. The backscatter of wheat canopies is modeled by a discrete approach, based on the radiative transfer theory and including multiple scattering effects. European Remote Sensing satellite synthetic aperture radar signatures and detailed ground truth, collected over wheat fields at the Great Driffield (U.K.) site, are used to test the model and train the networks. Multitemporal, multifrequency data collected by the Radiometer-Scatterometer (RASAM) instrument at the Central Plain site are used to test the retrieval algorithm.

Interferometric Point Target Analysis (IPTA) is a method to exploit the temporal and spatial characteristics of interferometric signatures collected from point targets to accurately map surface deformation histories, terrain heights, and relative atmospheric path delays. In this contribution the IPTA concept is introduced, including the point selection criteria, the phase model and the iterative improvement of the model parameters. Intermediate and final results of an IPTA example using a stack of ERS-1 and ERS-2 data, confirm the validity of the concept and indicate a high accuracy of the resulting products.

Interferometric Point Target Analysis (IPTA) is a method that exploits the temporal and spatial characteristics of interferometric signatures collected from point targets that exhibit long-term coherence to map surface deformation. This paper demonstrates the viability of this technique for L-band data collected by the JERS-1 sensor during the time period 1992-1998. A data set covering Koga, Japan is used for demonstration and indicates regions of substantial subsidence.

This paper describes an algorithm aimed at monitoring the soil moisture and the growth cycle of wheat fields using radar data. The algorithm is based on neural networks trained by an electromagnetic model and multitemporal ground data measured on fields taken as a reference. The retrieval procedure is tested using mutitemporal signatures collected at a test site.

Underground mining activities very often cause changes to the surface. Beside the strong influences of active mining, former mining activities with still existing cavities in the underground do influence the surface as well, causing a risk for people and infrastructure. The main risk is induced by sudden falls of the surface. In contrast to surface deformations induced by active mining these deformations are difficult to predict. The risk justifies the monitoring of ground movements in historical mining areas. One technique evaluated is Differential Interferometric Synthetic Aperture Radar (DInSAR). The main difference to other applications of DInSAR is, that location, subsidence rate and spatial extent can just roughly be estimated using related historic information about former mining activities. In addition, the expected character of the subsidence complicates the separation between deformation signal and atmospherical artifacts. In order to get a first idea about the limits of the technique, circular shaped deformation models with different extents and maximum vertical displacements have been modeled and integrated into the DInSAR processing chain. The final results have been evaluated qualitatively and quantitatively in order to get an idea about the detection limits of DInSAR using ERS-1/2 data. The qualitative assessment shows that subsidence cones of circular shape with radii below 400 m cannot be reliably identified in a single interferogram in the investigated case. In the quantitative assessment the highest relative RMS errors between model and result were found for subsidence cones with small radii and small maximum vertical displacements. Furthermore it could be shown that the use of multiple interferograms by applying stacking techniques leads to a better detection limit of the DInSAR method.

This contribution reports on the potential and limitations of L-band differential SAR interferometry for the detection and monitoring of unstable high-mountain slopes. SAR data of the JERS-1 satellite for the Swiss Alps have been analyzed together with ERS SAR data. Significant results for permafrost creep and rockslides after glacier retreat are presented.

In order to provide the best knowledge of the subsidence process around the Lagoon of Venice to the authorities that manage the area, SAR-based monitoring techniques (differential SAR interferometry and the interferometric point target analysis) will be integrated with levelling and GPS surveys into an overall database and information system. In this contribution the different monitoring techniques are briefly introduced, the results presented and compared, and the integration concept explained.

The X-SAR SRTM DEM of the alpine region in Switzerland was validated for SAR geocoding and 2-pass differential interferometry using ERS and JERS data. The absolute horizontal and vertical accuracies of the X-SAR SRTM DEM are in general in line with the nominal indications, but in very rugged areas we found horizontal offsets of up to 50 m in both northing and easting directions and some extreme absolute height errors up to even 1000 m. Also visible in the X-SAR SRTM DEM is an important noise for the slopes of SAR illumination. The ERS and JERS SAR images geocoded with the X-SAR SRTM DEM do not contain significant geometric artifacts, but, in parallel to what was discussed before, show in comparison to the map offsets in both easting and northing directions. The matching of geocoded products of descending and ascending modes is very good, i.e. on the order of 1 to 2 pixels. Considering that in our geocoding approach the geocoded SAR images are accurately co-registered to the DEM, the horizontal offsets have no influence for 2-pass differential interferometry. We found that the X-SAR SRTM DEM is well suited for ERS and JERS interferograms of the descending mode of baselines up to 100 m. For the differential interferometric analysis of ERS SAR data in ascending mode, however, attention has to be paid to the noise of the slopes facing away from the X-SAR.

In 2002 ENVISAT with the advanced SAR (ASAR) as one important instrument was launched. As compared to the preceding SAR sensors on ERS-1 and ERS-2 the ASAR has beam-steering capability permitting the acquisition of data over the same area under the different incidence angles as required by radargrammetry. In this paper a concept of combined use of radargrammetry and space triangulation to derive digital elevation models (DEMs) is presented. Space triangulation with opposite side acquisitions is used to determine a number of control points. Radargrammetry is then used to derive a DEM using these control points as reference. Preliminary test on the different steps of the combined method are presented.

In 2002 ENVISAT with the advanced SAR (ASAR) as one important instrument was launched. As compared to the preceding SARs on ERS-1 and ERS-2 the ASAR has additional functionality with many different modes. Important aspects of the ASAR include the alternating polarization modes for quasi simultaneous acquisition of data at two different polarizations, and beam-steering capability, which permits to acquire data at different incidence angles. In this paper the use of the ENVISAT ASAR for land cover information retrieval is discussed. For multi-incidence angle data a coefficient to characterize the incidence angle dependence is proposed.

In 2002 ENVISAT with the advanced SAR (ASAR) as one important instrument was launched. As compared to the preceding SARs on ERS-1 and ERS-2 the ASAR has additional functionality with many different modes. Important aspects of the ASAR include the alternating polarization modes for quasi simultaneous acquisition of data at two different polarizations, and beam-steering capability, which permits to acquire data at different incidence angles. To optimize the use of the new functionality adequate processing techniques are required. This is particularly important for data acquired in different swaths, i.e. with different image geometries. In this paper ASAR multi-swath techniques are discussed, including image co-registration, multi-channel filtering, and multi-swath multi-temporal techniques.

The objective of our investigation was to validate the potential of L-band SAR for the mapping of rapid subsidence and to demonstrate its monitoring capability for the specific case, Belridge in California USA. The results confirm the good potential of L-band repeat-pass interferometry to map and monitor subsidence. Subsidence rates of more than 46 cm per year were observed and mapped.

In this paper, the potential of L-band repeat-pass differential synthetic aperture radar (SAR) interferometry for land subsidence monitoring is evaluated using Japanese Earth Resources Satellite (JERS) SAR data. Bologna, Mexico City, and the Ruhrgebiet are selected as application sites representing slow to fast deformation velocities. The investigation includes feasibility aspects such as data availability, the temporal decorrelation over different landcover classes and the range of useful spatial baselines, an analysis of the achieved deformation accuracy, and considerations on the complementarity to European Remote Sensing satellite (ERS) SAR interferometry and leveling surveys. In spite of the rather limited data availability, land subsidence maps could be generated for the three selected application sites. In contrast to ERS C-band SAR data, JERS L-band interferometry permitted the retrieval of subsidence values over vegetated areas and forest when using interferograms of less than one year acquisition time interval and short baseline. In addition, the longer L-band wavelength was found to be superior in the case of large deformation gradients that lead to phase-unwrapping problems in C-band interferometry.

An algorithm, based on an electromagnetic model and a neural network, aimed at monitoring the multitemporal evolution of wheat fields, is described. Three different sites are used to validate the model, provide reference ground data, and test the algorithm.

Potential and limitations of differential SAR interferometry for the periodical survey of Alpine displacements are investigated. For the Alpine territory, which is characterized by low vegetation, C-band differential SAR interferometry shows a relatively high coherence during the snow-free season permitting the detection and monitoring of unstable slopes on a regular basis. Limitations to the spatial coverage arise from decorrelation over forested and snow covered areas and from layover and shadowing caused by the very rugged topography. Significant results in the Swiss Alps using ERS-1/2 SAR data are used as support to our investigations.

Space-borne SAR has a good potential to support disaster management and humanitarian relief projects thanks to its all-weather capability, its capabilities for change detection, the large existing data archives, and, of course, the geometric and thematic information content of the images. Basic processing techniques, including precision image co-registration, multi-temporal analysis, terrain corrected geocoding, interferometry, and radargrammetry, are well developed and operational. This contribution is an assessment of possible roles of space-borne SAR and in particular ENVISAT ASAR in disaster management and humanitarian relief projects. Relevant additional functionality of ASAR as compared to ERS-1/2 comes from the beam-steering, multipolarization, larger incidence angle, wide-angle, and scan-SAR capability.

Quantitative analyses of remote sensing data acquired with variable geometries and different sensors are greatly facilitated by geocoding the data to a common geographical reference. Errors in geocoded SAR images are due to inaccuracies in the orbit data, errors in the processing parameters, and DEM errors. Automated terrain geocoding of SAR data is possible using only a single resampling of the native slant-range to the final map geometry after measurement and correction for residual geolocation errors. An accurate adaptive resampling algorithm for the interpolation from the SAR geometry to the map coordinate system is presented. This processing paradigm has been extended to permit combining data from different SAR instruments.

Two image-to-image patch offset techniques for estimating feature motion between satellite synthetic aperture radar (SAR) images are discussed. Intensity tracking, based on patch intensity cross-correlation optimization, and coherence tracking, based on patch coherence optimization, are used to estimate the movement of glacier surfaces between two SAR images in both slant-range and azimuth direction. The accuracy and application range of the two methods are examined in the case of the surge of Monacobreen in Northern Svalbard between 1992 and 1996. Offset-tracking procedures of SAR images are an alternative to differential SAR interferometry for the estimation of glacier motion when differential SAR interferometry is limited by loss of coherence, i.e. in the case of rapid and incoherent flow and of large acquisition time intervals between the two SAR images. In addition, an offset-tracking procedure in the azimuth direction may be combined with differential SAR interferometry in the slant-range direction in order to retrieve a two-dimensional displacement map when SAR data of only one orbit configuration are available.

Two landuse maps and a forest map of three different areas in Europe were completed with ERS SAR interferometry. The test sites represent various geomorphological regions with different cover types. In this article, the mapping algorithms are presented, the results are summarized, and the potential and limitations of ERS SAR interferometry for landuse mapping are discussed. Overall, the results suggest that landuse classification accuracies on the order of 75% are possible with, in the best case, simultaneous forest and nonforest accuracies of around 80-85%. The presence of topography reduces the performance.

Repeat pass ERS SAR interferometry was used to map different surface types of the Death Valley Salt Pan. At this arid site, the surface roughness varies between extremely smooth (salt lakes, clay pan) and very rough (alluvial fans, eroded salt formations). The moisture varies from very dry (alluvial fans) to wet (wet clay, salt crust with water ponds) with seasonal changes In certain areas. While the surface geometry is stable for certain surface types (alluvial fans, thick salt crusts), it changes for others as a result of the changing water level and erosion by wind and rain. The changing surface roughness, moisture, and vegetation conditions cause large variations of the backscattering and coherence. This information is used to distinguish different surface types. Furthermore, interferometry was used to map the topographic height. In a second part of the study, the potential of the degree of coherence to retrieve information on the vegetation density and the surface erosion was evaluated.

A semiempirical model for the reflectivity of rough bare soil is presented. One of the main objectives of this new model development was to derive a simple model with few model parameters and a wide applicability. A large number of ground-based measurements in the 1-100-GHz range at H and V-polarization and incidence angles between 20/spl deg/ and 70/spl deg/ were used for the model development.

The potential of SAR interferometric techniques for the retrieval of vegetation parameters was investigated using ERS-1 data over agricultural and forested test sites. In a first experiment an interferometrically derived forest map was generated. The classification was based on the interferometric correlation and the backscatter intensities. The result was geocoded, using the interferometrically derived height map generated from the same ERS SAR data pair, and validated with a conventional digital forest map. Forest mapping accuracies of around 90% and better were achieved. In a second experiment, multitemporal data over an agricultural site were used to investigate the potential of repeat-pass interferometry to monitor farming activity, crop development, and soil moisture variations. The interferometric correlation was used as an indicator of dense vegetation and geometric change. It was possible, for example, to identify harvesting by the high correlation of the post-harvest bare or stubble field. Decreasing interferometric correlation was observed as a consequence of crop growth.

The potential of SAR interferometry for forest mapping and monitoring is discussed. It is shown that forest can clearly be discriminated from other land categories. Furthermore it is possible to distinguish a number of forest types. The presented approach is based on the SAR interferometric correlation and the backscatter intensities using ERS-1 SAR repeat-pass data. Baseline, time interval, and seasonal dependences were analyzed, substantiating a wide applicability of the approach. Data over an Alaskan test site were used to extend the results found over temperate forest to boreal forest and to demonstrate the potential of the described technique over remote areas. In addition, repeat-pass SAR interferometry was found to be particularly sensitive to change. Examples for the recognition of freezing, mechanical cultivation of agricultural fields, and canopy growth are shown.>

The ground-based radiometer-scatterometer system of the University of Bern, RASAM, measures brightness temperatures and backscattering coefficients at frequencies between 2 and 12 GHz. Between 1984 and 1992 many campaigns were carried out on agricultural fields. Data from more than 70 bare agricultural soils have now been organized in a bare soil signature catalogue. In addition to the microwave data, detailed ground-truth information was collected including highly accurate surface roughness profiles. Information on the data base is given, together with short descriptions of the instruments and measurement procedures.>