SAOCOM-1 L-Band DInSAR Time Series Generation Through the P-SBAS Approach: Algorithm Extension and Products Analysis

We present in this work the new advances of the differential synthetic aperture radar (SAR) interferometry (DInSAR) technique referred to as the parallel Small BAseline Subset (P-SBAS) approach for the generation of displacement time series from the recently available L-band SAR image sequences acquired by the Argentinian SAOCOM-1 constellation. In particular, we first discuss the algorithmic extensions allowing us to retrieve SAOCOM-1 DInSAR products, with a main focus on the exploitation of Stripmap SAR images and multilook interferograms. These algorithmic improvements are relevant to the adjacent single look complex (SLC) image slices merging to the multitemporal residual orbital artifacts mitigation and to the phase unwrapping procedure enhancement. Subsequently, the effectiveness of the extended P-SBAS processing chain is shown through a comparative analysis between the achieved SAOCOM-1 DInSAR results and the corresponding ones relevant to Sentinel-1 and COSMO-SkyMed acquisitions. Moreover, a comparison between the SAOCOM-1 displacement time series and the GNSS measurements, relevant to the Campi Flegrei Caldera (Italy), is presented. This shows that the mean standard deviation value of the differences between the DInSAR and the GNSS time series, the latter projected in the radar line of sight, is about 1 cm, further confirming the effectiveness of the P-SBAS processing chain extension.

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Publication Details

Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Published
2024-11-27
DOI
https://doi.org/10.1109/jstars.2024.3507554
Citations
6
Primary Topic
Time Series Analysis and Forecasting
Type
article
Field-Weighted Citation Impact
2.10
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article

SAOCOM-1 L-Band DInSAR Time Series Generation Through the P-SBAS Approach: Algorithm Extension and Products Analysis

Michele Manunta, Deodato Tapete, Muhammad Yasir, Yenni Lorena Belen Roa et al.
6 citations
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Time Series Analysis and Forecasting
2.10
article

SAOCOM-1 L-Band DInSAR Time Series Generation Through the P-SBAS Approach: Algorithm Extension and Products Analysis

Michele Manunta, Deodato Tapete, Muhammad Yasir, Yenni Lorena Belen Roa, Claudio De Luca, Leonardo Euillades, Francesco Casu, Riccardo Lanari, Manuela Bonano, Pasquale Striano, Pablo Euillades, Luigi Dini, Marianna Franzese, G. Onorato
article en
6 citations

Abstract

We present in this work the new advances of the differential synthetic aperture radar (SAR) interferometry (DInSAR) technique referred to as the parallel Small BAseline Subset (P-SBAS) approach for the generation of displacement time series from the recently available L-band SAR image sequences acquired by the Argentinian SAOCOM-1 constellation. In particular, we first discuss the algorithmic extensions allowing us to retrieve SAOCOM-1 DInSAR products, with a main focus on the exploitation of Stripmap SAR images and multilook interferograms. These algorithmic improvements are relevant to the adjacent single look complex (SLC) image slices merging to the multitemporal residual orbital artifacts mitigation and to the phase unwrapping procedure enhancement. Subsequently, the effectiveness of the extended P-SBAS processing chain is shown through a comparative analysis between the achieved SAOCOM-1 DInSAR results and the corresponding ones relevant to Sentinel-1 and COSMO-SkyMed acquisitions. Moreover, a comparison between the SAOCOM-1 displacement time series and the GNSS measurements, relevant to the Campi Flegrei Caldera (Italy), is presented. This shows that the mean standard deviation value of the differences between the DInSAR and the GNSS time series, the latter projected in the radar line of sight, is about 1 cm, further confirming the effectiveness of the P-SBAS processing chain extension.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingVol. 18
Universidad Nacional de Cuyo (AR), Agenzia Spaziale Italiana (IT), Istituto per il Rilevamento Elettromagnetico dell'Ambiente (IT), University of Naples Federico II (IT)
Openalex Percentile: Top 11%
Time Series Analysis and Forecasting
2.10
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