Assessment of Mitigation Strategies for Tropospheric Phase Contributions to InSAR Time-Series Datasets over Two Nicaraguan Volcanoes

Abstract

Interferometric Synthetic Aperture Radar (InSAR) studies of ground displacement are often plagued by tropospheric artifacts, which are phase delays resulting from spatiotemporal variations in the refractivity of air within the troposphere. In this study, we focus on COSMO-SkyMed (X-band) InSAR products obtained over two different types of volcanoes in Nicaragua: the Telica stratovolcano and the Masaya caldera. We examine the applicability of an empirical linear correction method and three Global Weather Models (GWMs) with different spatial and temporal resolutions for removing the tropospheric phase component. We linearly invert the tropospheric-corrected interferograms using the Small BAseline Subset (SBAS) time-series technique to produce time-series of ground displacement. Statistical assessments were performed on the corrected interferograms to examine the significance of the applied corrections on the individual interferograms and time-series results. We find that the applicability of the correction methods is highly case-dependent and that in general, the temporal resolution of GWMs influences their ability to capture turbulent tropospheric phase delays. At the two target volcanoes, our study shows that none of the GWMs are able to accurately capture the tropospheric phase delays. Our study provides a guide for researchers using InSAR data in tropical regions who wish to use tropospheric model corrections to carefully assess the applicability of the different types of tropospheric correction methods.Remote Sens. 2020, 12, 782 2 of 30 which include the true displacement of the ground (∆φ de f ), differences in the orbital geometry of the satellite (∆φ orbit ), contributions of the topography (∆φ topo ), phase delay contributions from the ionosphere and troposphere (∆φ atm ), and other noise contributions such as instrument noise and changes in scattering properties of the ground (∆φ noise ). Topographic contributions can be accounted for by using digital elevation models [13] and orbital geometry contributions can be removed using precise satellite orbits (also known as flattening). With the improvement in satellite technology, thermally-correlated noise contributions from the instruments onboard the satellites can be assessed [14,15]. Poor coherence (low signal-to-noise ratio) pixels due to changes in the scattering properties of the ground can be masked from the ground displacement field.Atmospheric contributions, however, are more difficult to account for as they often involve phase delay contributions from both the ionosphere (60-1000 km) and troposphere (0-10 km), which can bias true ground displacement measurements [12,16]. Ionization of neutral atoms and molecules in the ionosphere from high energy solar radiation results in a mix of neutral gas molecules, free ions and electrons [17]. This influences the propagation of microwave radiation through the ionospheric layer, causing phase shifts such as phase advances and group delays, which translate as azimuth shifts and defocusing between SAR acquisitio...

Automatic Detection of Volcanic Surface Deformation Using Deep Learning

“…region (Stephens et al, 2020). The InSAR time series closely follows the GPS LOS displacement within the first two months of lava lake activity, whereas the CNN overestimates the LOS displacement.…”

“…Surface deformation maps of volcanic regions within the Central American Volcanic Arc are often plagued by atmospheric phase delays from the troposphere, and this distorts measurements of noise‐free surface deformation associated with volcanic activity (Ebmeier et al, 2013; Stephens et al, 2020). For this study, we focus our efforts on Masaya caldera, a basaltic caldera located approximately 20 km SE of Managua City in Nicaragua.…”

“…Atmospheric phase delay signals in InSAR products are particularly challenging to mitigate due to the complex spatial and temporal variations in atmospheric parameters such as hydrostatic pressure, temperature, and concentrations of water vapor (Emardson et al, 2003; Hanssen, 2001; Remy et al, 2003). Stratified and turbulent atmospheric phase contributions may greatly complicate the interpretation of deformation signals (Doin et al, 2009; Ebmeier, 2016; Stephens et al, 2020). In order to conduct near real‐time volcano monitoring on a global basis, an automatic processing tool for detecting volcanic deformation signals through denoising interferograms and resulting InSAR time series products is necessary.…”

“…Using variant combinations of training data set and applying stratified atmospheric correction in advance may reduce the risk of false positives to some extent, and the detailed discussions can be found in Anantrasirichai et al (2019a). However, removing stratified atmospheric effects using global weather models remains a significant challenge that was recently shown to be ineffective at several Central American volcanoes (Stephens et al, 2020).…”

Baseline monitoring of volcanic regions with little recent activity: application of Sentinel-1 InSAR to Turkish volcanoes

“…Examples of atmospheric corrections to individual interferograms from track 167 which covers Hasandağ, Acıgöl-Nevsehir and Göllüdağ and encompasses elevations from 600 m-3600 m. a-c) example where b) the GACOS correction works better than c) the phase-elevation correction; d-f) example where e) the GACOS correction works worse than f) the phase-elevation correction However, each of these has advantages and disadvantages when applied to individual volcanoes (e.g. Stephens et al 2020), and the priority for volcano monitoring is confidently identifying which signals are atmospheric artefacts, and which are really deformation.…”