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Article

Critical Factors for the Application of InSAR Monitoring in Ports

by
Jaime Sánchez-Fernández
1,2,3,
Alfredo Fernández-Landa
2,3,*,
Álvaro Hernández Cabezudo
2,3 and
Rafael Molina Sánchez
4
1
Escuela Técnica Superior de Ingenieros Navales, Universidad Politécnica de Madrid (UPM), Av. Universidad Politécnica de Madrid, 28040 Madrid, Spain
2
Department of Land Morphology & Engineering, ETSI Caminos, Canales y Puertos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
3
Detektia Earth Surface Monitoring S.L., C/Faraday 7, 28049 Madrid, Spain
4
Canal de Ensayos Hidrodinámicos del grupo de investigación de la Escuela Técnica Superior de Ingenieros Navales (ETSI Navales) UPM, DITTU, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3900; https://doi.org/10.3390/rs17233900 (registering DOI)
Submission received: 1 October 2025 / Revised: 14 November 2025 / Accepted: 28 November 2025 / Published: 30 November 2025

Abstract

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations (σr,σa,σc) are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows.
Keywords: InSAR monitoring; port infrastructure; LiDAR–InSAR fusion; persistent and distributed scatterers; deformation time series; RGB composites; shadow and layover masking InSAR monitoring; port infrastructure; LiDAR–InSAR fusion; persistent and distributed scatterers; deformation time series; RGB composites; shadow and layover masking

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MDPI and ACS Style

Sánchez-Fernández, J.; Fernández-Landa, A.; Hernández Cabezudo, Á.; Molina Sánchez, R. Critical Factors for the Application of InSAR Monitoring in Ports. Remote Sens. 2025, 17, 3900. https://doi.org/10.3390/rs17233900

AMA Style

Sánchez-Fernández J, Fernández-Landa A, Hernández Cabezudo Á, Molina Sánchez R. Critical Factors for the Application of InSAR Monitoring in Ports. Remote Sensing. 2025; 17(23):3900. https://doi.org/10.3390/rs17233900

Chicago/Turabian Style

Sánchez-Fernández, Jaime, Alfredo Fernández-Landa, Álvaro Hernández Cabezudo, and Rafael Molina Sánchez. 2025. "Critical Factors for the Application of InSAR Monitoring in Ports" Remote Sensing 17, no. 23: 3900. https://doi.org/10.3390/rs17233900

APA Style

Sánchez-Fernández, J., Fernández-Landa, A., Hernández Cabezudo, Á., & Molina Sánchez, R. (2025). Critical Factors for the Application of InSAR Monitoring in Ports. Remote Sensing, 17(23), 3900. https://doi.org/10.3390/rs17233900

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