First results using the new Sentinel-1 SAR look very promising but the special interferometric wide-swath data acquired in the TOPS mode makes InSAR processing more challenging than for normal stripmap mode data. The steep azimuth spectra ramp in each burst results in very stringent co-registration requirements. Combining the data of the individual bursts and sub-swaths into consistent mosaics requires careful “book-keeping” in the handling of the data and meta data and the large file sizes and high data throughputs require also a good performance. Considering these challenges good support from software is getting increasingly important. In this contribution we describe the Sentinel-1 support in the GAMMA Software, a high-level software package used by researchers, service providers and operational users in their SAR, InSAR, PSI and offset tracking work

Standard focusing of data from synthetic aperture radar (SAR) assumes a straight recording track of the sensor platform. Small nonlinearities of airborne platform tracks are corrected for during a motion-compensation step while maintaining the assumption of a linear flight path. This paper describes the processing of SAR data acquired from nonlinear tracks, typical of sensors mounted on small aircraft or drones flying at low altitude. Such aircraft do not fly along straight tracks, but the trajectory depends on topography, influences of weather and wind, or the shape of areas of interest such as rivers or traffic routes. Two potential approaches for processing SAR data from such highly nonlinear flight tracks are proposed, namely, a patchwise frequency-domain processing and mosaicking technique and a time-domain back-projection-based technique. Both are evaluated with the help of experimental data featuring tracks with altitude changes, a double bend, a 90deg curve, and a linear flight track. In order to assess the quality of the focused data, close-ups of amplitude images are compared, impulse response functions of a point target are analyzed, and the coherence is evaluated. The experimental data were acquired by the German Aerospace Center's E-SAR L-band system.

Forest canopies are semitransparent to microwaves at both L- and P-bands. Thus, a number of scattering sources and different types of scattering mechanisms may contribute to a single range cell of a synthetic aperture radar (SAR) image. By appropriately combining the SAR data of multiple parallel flight paths, a large 2-D aperture is synthesized, which allows for tomographic imaging of the 3-D structure of such semitransparent media and the underlying ground. A separate paper deals with the actual tomographic imaging part that leads to the 3-D data cube. In particular, three focusing techniques are described and analyzed: multilook beamforming, robust Capon beamforming, and multiple signal classification beamforming. In this paper, the resulting data products obtained by tomographically focusing two airborne multibaseline SAR data sets of a partially forested area, one at L-band and another at P-band, are subject to a detailed analysis with respect to the location and the type of backscattering sources. In particular, the following aspects are investigated: 1) The forest structure, as obtained from the vertical profiles of intensities at sample plot locations within the forest, is compared to the height distribution of the top of the forest canopy, as derived from airborne laser scanning data, and profiles are presented for all polarimetric channels and focusing techniques, as well as at both frequencies; 2) the type and location of scattering mechanisms are analyzed as functions of height for the two frequencies, namely, L- and P-bands, and using the polarimetric channels, as well as the Pauli and CloudePottier decompositions thereof; and 3) the accuracy of the ground elevation estimation obtained from the different focusing techniques and the two frequencies is assessed with the help of a lidar-derived digital elevation model.

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.

Airborne Laser Scanning (ALS) has been established as a valuable tool for the estimation of biophysical vegetation properties such as tree height, crown width, fractional cover and leaf area index (LAI). It is expected that the conditions of data acquisition, such as viewing geometry and sensor configuration influence the value of these parameters. In order to gain knowledge about these different conditions, we test for the sensitivity of vegetation products for viewing geometry, namely flying altitude and scanning (incidence) angle. Based on two methodologies for single tree extraction and derivation of fractional cover and LAI previously developed and published by our group, we evaluate how these variables change with either flying altitude or scanning angle. These are the two parameters which often need to be optimized towards the best compromise between point density and area covered with a single flight line, in order to reduce acquisition costs. Our test‐site in the Swiss National Park was sampled with two nominal flying altitudes, 500 and 900 m above ground. Incidence angle and local incidence angle were computed based on the digital terrain model using a simple backward geocoding procedure. We divided the raw laser returns into several different incident angle classes based on the flight path data; the TopoSys Falcon II system used in this study has a maximum scan angle of ±7.15°. We compared the derived biophysical properties from each of these classes with field measurements based on tachymeter measurements and hemispherical photographs, which were geolocated using differential GPS. It was found that with increasing flying height the well‐known underestimation of tree height increases. A similar behaviour can be observed for fractional cover; its respective values decrease with higher flying height. The minimum distance between first and last echo increases from 1.2 metres for 500 m AGL to more than 3 metres for 900 m AGL, which does alter return statistics. The behaviour for incidence angles is not so evident, probably due to the small scanning angle of the system used. fCover seems to be most affected by incidence angles, with significantly higher differences for locations further away from nadir. As expected, incidence angle appears to be of higher importance for vegetation density parameters than local incidence angle.

Riverbank erosion occurs along many of the Earth's river systems, affecting riverine populations by destroying agricultural land and housing. In this study, we detected past events of riverbank erosion along the Jamuna River in Bangladesh using time series of Sentinel-1 satellite radar imagery, ground-range-detected (GRD) data with a 12 d revisit cycle, available in the Google Earth Engine (GEE). Eroded land is detected by performing a land cover classification and by detecting land cover changes from vegetated areas before the monsoon to sand or water after the monsoon. Further, settlements are detected as persistent scatterers and classified as eroded if they are located on eroded land. We found that with Sentinel-1 data, erosion locations can be determined already 1 month after the end of the monsoon and hence potentially earlier than using optical satellite images which depend on cloud-free daylight conditions. Further, we developed an interactive GEE-based online tool allowing the user to explore where riverbank erosion has destroyed land and settlements along the Jamuna in five monsoon seasons (2015–2019). The source code of our implementation is publicly available, providing the opportunity to reproduce the results, to adapt the algorithm and to transfer our results to assess riverbank erosion in other geographical settings.