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A geostatistical approach for digital elevation InSAR modelling based on SAOCOM and Sentinel-1 data. Case study: Córdoba, Argentina

Authors

Ibarra,  Frederico
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Chiarito,  Eugenia
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Micou,  Ana Paula
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Seco,  José Luis
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Ludueña,  Sebastian
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Nunnini,  Luciano
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Cimbaro,  Sergio
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Citation

Ibarra, F., Chiarito, E., Micou, A. P., Seco, J. L., Ludueña, S., Nunnini, L., Cimbaro, S. (2023): A geostatistical approach for digital elevation InSAR modelling based on SAOCOM and Sentinel-1 data. Case study: Córdoba, Argentina, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4280


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5021716
Abstract
A methodology for DEM estimation is presented, tested in a 1600km2 region of Córdoba, Argentina, with mountainous topography to the west (12.5% mean slope) and flatland to the east (mean slope 2.5%), as well as variable vegetation cover, from forest to crops. A set of 54 Sentinel-1 and 12 SAOCOM-1 individual DEMs were obtained. Within each sensor set, unreliable data was filtered by acknowledging high NDVI or low coherence values, and the median altitude of the remaining pixels was calculated. Other statistical approaches, such as weighted average and global means were tested, with lower accuracy. As a result, 2 DEMs were obtained with 15 m pixel resolution. The planimetric position was corrected and lineal global vertical adjustment was performed, based on 65 topographic pillars’ position data from the Argentine vertical reference frame network (RN-Ar). Finally, a smoothing filter was performed to replace noisy outliers. Results were compared to a photogrammetric airborne DEM of 5 m resolution and sub-metric vertical precision. SAOCOM DEM showed a mean error of 0.34m and 3.76m standard deviation. Sentinel-1 DEM presented 1.46m mean error, and 7.35m standard deviation, most probably because of volumetric decorrelation due to vegetation. More tests are being performed to better select the most reliable pixels from the dataset, by introducing phase and height of ambiguity analysis. Furthermore, sensors data fusion, vertical nonlinear enhancement, and longer datasets will be tested.