Masters Theses

Abstract

This research develops an empirical model to characterize spatial-temporal InSAR errors and improve the accuracy of deformation time-series analysis. Using standardized Sentinel-1 HyP3 products and MintPy, the study quantifies how correlated noise affects velocity precision and validates the results against continuous GNSS velocities.

Residual velocities are near-Gaussian, with σ ~0.92–2.04 cm/yr and ~1 cm/yr on average. Variograms show power-law spatial structure with a non-zero nugget implicating troposphere and decorrelation while errors drop exponentially with more acquisitions; spatial uncertainty is strongly affected by unwrapping errors, coherence, and tropospheric noise, not simply troposphere.

A comparative assessment of on-demand cloud processing with other methodologies such as PS+DS and commercial softwares suggests that on-demand processing retains some noise due to unwrapping errors and decorrelation that other processing methodologies can correct, e.g., PS+DS achieved the lowest standard deviation (~0.27 cm/yr). However, on-demand processing can achieve better results when using long time-periods.

Overall, the empirical approach developed here provides a quantitative framework for describing and modeling spatial-temporal InSAR errors. The findings support the integration of empirical noise modeling, GNSS validation, and consistent processing standards to improve deformation monitoring across geodetic, volcanic, and environmental applications.

Advisor(s)

Maurer, Jeremy

Committee Member(s)

Liu, Kelly H.
Gao, Stephen S.

Department(s)

Geosciences and Geological and Petroleum Engineering

Degree Name

M.S. in Geological Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2026

Pagination

ix, 30 pages

Note about bibliography

Includes_bibliographical_references_(pages 27-29)

Rights

© 2026 Godwin Naaba Ndaa , All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12601

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