# An Improved SBAS-InSAR Processing Method Considering Phase Consistency: Application to Landslide Monitoring in Hualong County, Qinghai Province, China

## TL;DR

This paper introduces a new InSAR method to improve phase consistency for better landslide monitoring in Hualong County, China.

## Contribution

The novel NCP-SBAS workflow enforces phase consistency to correct fading biases and unwrapping errors in deformation estimation.

## Key findings

- Fading biases affect deformation estimates based on magnitude and direction.
- Unwrapping errors lead to underestimation of deformation.
- NCP-SBAS shows significant advantages in landslide monitoring.

## Abstract

Phase consistency is a critical prerequisite for achieving high-precision time-series InSAR deformation retrieval. However, conventional SBAS-InSAR methods provide only limited consideration of phase consistency during the inversion process. Within the SBAS-InSAR workflow, two principal categories of error sources are primarily responsible for phase inconsistency, manifested as non-zero closure phase (NCP): (1) fading biases introduced during multilooking and filtering prior to phase unwrapping; and (2) unwrapping errors caused by large deformation gradients, low coherence, or inappropriate selection of unwrapping algorithms. To address these issues, this study introduces an improved SBAS-InSAR processing workflow, termed NCP-SBAS, designed to improve the accuracy of deformation field estimation and thereby enhance its applicability to geological hazard monitoring. The key idea of the method is to enforce phase consistency as a constraint, jointly accounting for the spatiotemporal characteristics of fading biases and the valid deformation signals, thereby enabling effective correction of NCP. To evaluate the effectiveness of NCP-SBAS, this study conducted a detailed analysis of deformation differences in Hualong County, Qinghai Province, before and after NCP correction, highlighting the significant advantages of the proposed approach. The results indicate that the influence of fading biases on deformation estimates depends on both the magnitude and direction of deformation, while unwrapping errors primarily lead to an underestimation of deformation. In addition, the study provides an in-depth discussion of how fading biases and unwrapping errors affect landslide monitoring and identification.