Large-Scale Characterisation and Correction of the InSAR Phase Bias: Insights from Nationwide Analysis in Italy

While phase bias in interferometric synthetic aperture radar (InSAR) can provide useful insights into temporal changes in geophysical variables such as soil moisture and vegetation dynamics, it can also introduce systematic errors in the interferometric phase. These biases can severely distort displacement time series and lead to unreliable velocity estimates, particularly when using short-baseline multilooked interferograms. Phase linking (PL) techniques can mitigate InSAR phase biases, but their applicability is often limited in regions with low long-term coherence, such as densely vegetated or seasonally dynamic landscapes.

In this work, we apply our recently developed InSAR phase bias correction algorithm—originally validated over selected test sites—to the entire Italian peninsula, demonstrating its robustness and scalability in operational contexts. The algorithm estimates bias terms from short-term wrapped interferograms using calibration factors derived from long-term interferograms and includes a temporal smoothing constraint to manage time-series gaps. This large-scale implementation enables us to analyse the spatial and temporal behaviour of phase bias across diverse land cover types and climatic zones.

We systematically examine how phase bias varies across forests, agricultural lands, and urban regions, and how its characteristics evolve seasonally. Our results show that phase bias effects are most pronounced in vegetated and moisture-sensitive regions during wet seasons, often manifesting as false subsidence or uplift in uncorrected velocity fields. Corrected velocity maps demonstrate strong alignment with those from PL methods in high-coherence areas while preserving meaningful deformation signals in regions where PL fails due to decorrelation.

This study presents a large-scale quantification and correction of InSAR phase bias using a non-PL-based strategy, offering a practical alternative for deformation monitoring in challenging environments. Our findings highlight the importance of incorporating phase bias correction in regional-scale InSAR applications, particularly for tectonic, volcanic, and hydrological hazard monitoring in areas where long-term coherence cannot be guaranteed.