On the P-SBAS Processing Chain New Developments For The Generation Of SAOCOM-1 Advanced DInSAR Products

CLAUDIO DE LUCA; ROA, YENNI; MANUELA BONANNO; FRANCESCO CASU; EUILLADES, LEONARDO; EUILLADES, PABLO; MARIANNA FRANZESE; MICHELE MANUNTA; YASIR MUHAMMAD; GIOVANNI ONORATO; PASQUALE STRIANO; IVANA ZINNO; RICCARDO LANARI

Leeds

Workshop; Fringe 2023; 2023

European Space Agency

In the current Earth Observation scenario the DifferentialSynthetic Aperture Radar Interferometry (DInSAR) technique has reached a key rolethanks to its ability to investigate surface displacements affecting largeareas of the Earth, with centimeter- to millimeter-level accuracy and ratherlimited costs, in both natural and anthropogenic hazard scenarios [1].Originally developed to analyze single deformation episodes such as anearthquake [2] or a volcanic unrest event [3], the DInSAR methods are also capableto investigate the temporal evolution of the surface deformations. Indeed, theso-called advanced DInSAR techniques properly combine the information availablefrom a set of multi-temporal interferograms relevant to an area of interest, inorder to compute the corresponding deformation time series [4-5]. Among severaladvanced DInSAR algorithms, a widely used approach is the one referred to asSmall BAseline Subset (SBAS) technique [5] and to its computationally efficientalgorithmic solution referred to as Parallel Small BAseline Subset (P-SBAS)technique [6].In this work, we show the results achievedwithin the project referred to as DInSAR-3M, funded by the Italian Space Agency(ASI), which is aimed to improve the generation, through advanced DInSARmethodologies, of multi-frequency surface deformation time series and meanvelocity maps, spatially and temporally dense, for the multi-scale analysis ofnatural and anthropogenic phenomena.In particular, we present several improvementsof the available P-SBAS processing chain which were necessary to effectively generateadvanced DInSAR products from SLC stripmap SAR image temporal sequences(Level-1A products) acquired by the twin L-band sensors of the Argentinian SAOCOM-1constellation. Specifically, we focus in the following onthe two steps to which most of the activities have been devoted. The first oneallows us to generate the SLC products specifically relevant to the zone to beinvestigated, referred hereafter to as area of interest (AoI), and the secondone, which allow us to improve the quality of the orbital information.For what concerns the implementation of theAoI SLCs generation, we remark that the SAOCOM-1 L1 images are made availablethrough “slices”, having a typical azimuth extension of about 80/100 km.Accordingly, particularly for large scale DInSAR analysis, they have to be properlymerged into a single SLC image relevant to the AoI. This slice-mergingoperation, which is an ordinary procedure in DInSAR scenarios, is unfortunatelynot straightforward for the SAOCOM SLC data. Indeed, two sub-steps have beenimplemented, which we refer as:•       Slice resampling on a commontemporal grid;•       Phase shift estimation and compensation.Aboutthe slices resampling on a common temporal grid procedure,it is important to highlight that different slices of the same SAOCOM-1acquisition are characterized by the same Pulse Repetition Frequency (PRF) butthey typically show slightly shifted temporal references. Accordingly, aresampling step is needed to properly align the timing of successive slices tobe subsequently fused in a single slice. Moreover, in order to finalize theslice images merging procedure, it is also necessary to carry out a phaseshift estimation and compensation step. Indeed, following thetemporal resampling of adjacent SLC slices, phase inconsistencies may appearwhen generating DInSAR interferograms, due to unexpected phase offsets betweenadjacent slices belonging to the same SAOCOM acquisition (see Fig. 1 of the attached file).To better clarify this issue, in Fig. 1-(c) we show an example of a 300 km azimuth extendeddifferential interferogram over the Piemonte region in Italy. As evident in Fig. 1-(c) and even morein Fig. 1-(d,e,f),the result of the merging procedure is affected by phase jumps, which may havea negative impact on the phase unwrapping procedure and, therefore, on thedisplacements retrieval operation. Fortunately, the presence of a significantoverlap between adjacent slices (see Fig. 1-(a,b)) allows us to easily estimate the existing phaseshift, which we can identify in correspondence of the peak of the SLC’s phasedifference histogram. In Fig.1-(g,h) we report the differential interferometric phase and the correspondinginterferometric coherence after applying the above discussed phase compensationprocedure, which properly accounts for the phase difference between adjacentslices.Finally, for a high qualityinterferograms generation, the implementation of a second step was needed.Indeed, the orbital information of the SAOCOM-1 SAR images are oftencharacterized by a low accuracy. Accordingly, if no orbital correction isapplied this unavoidably leads to an incorrect estimation of the topographicphase component within the DInSAR interferogram generation process and,therefore, it introduces artefacts in theinterferometric phase (that, at the first order, can be represented by a sortof phase ramp) which may significantly degrade the quality of the DInSAR productsif no appropriate correction is introduced. Accordingly, in order toimprove the quality of the generated DInSAR interferograms, we have implementedan additional step within the P-SBAS processing chain; this follows therationale of the algorithm described in [8], by properly exploiting theredundancy of the generated interferograms and retrieving an orbit correctionfor each single SAR acquisition of the exploited dataset. At the conference time we will present the P-SBAS results achievedby processing multi-temporal SAOCOM-1 image datasets relevant to differenthazard scenarios. In particular, we will show the results retrieved for areas affectedby slow-moving hydrogeological phenomena (Tuscany region, central Italy), and overvolcanic zones (Campi Flegrei Caldera, Mt. Etna and Stromboli volcano, southernItaly), thus highlighting the effectiveness of the implemented new developmentsof the P-SBAS processing chain.