Evaluation of Digital Elevation Models (DEM) Generated from the InSAR Technique in a Sector of the Central Andes of Chile, Using Sentinel 1 and TerraSar-X Images

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX.

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Journal
Applied Sciences
Published
2025-12-30
DOI
https://doi.org/10.3390/app16010392
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
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article
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article

Evaluation of Digital Elevation Models (DEM) Generated from the InSAR Technique in a Sector of the Central Andes of Chile, Using Sentinel 1 and TerraSar-X Images

Francisco Mena, Patricia Oliva, F. Coronel Flores, Paulina Vidal-Páez et al.
Applied Sciences
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Evaluation of Digital Elevation Models (DEM) Generated from the InSAR Technique in a Sector of the Central Andes of Chile, Using Sentinel 1 and TerraSar-X Images

Francisco Mena, Patricia Oliva, F. Coronel Flores, Paulina Vidal-Páez, Waldo Pérez-Martínez
article en

Abstract

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX.

Applied SciencesVol. 16(1)
Universidad Mayor (CL), University of Kaiserslautern (DE), Universidad de Alcalá (ES), GFZ Helmholtz Centre for Geosciences (DE), Chilean Air Force (CL), Universitat Politècnica de València (ES)
Openalex Percentile: Top 20%
Synthetic Aperture Radar (SAR) Applications and Techniques
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Evaluation of Digital Elevation Models (DEM) Generated from the InSAR Technique in a Sector of the Central Andes of Chile, Using Sentinel 1 and TerraSar-X Images — Francisco Mena, Patricia Oliva, et al. · Applied Sciences (2025) | TGRS Research Map | TGRS