Generic Atmospheric Correction Model for Interferometric Synthetic Aperture Radar Observations
Published in Journal of Geophysical Research: Solid Earth, 2018
Yu, C., Li, Z., Penna, N. T., & Crippa, P.
Abstract
For mapping Earth surface movements at larger scale and smaller amplitudes, many new synthetic aperture radar instruments (Sentinel‐1A/B, Gaofen‐3, ALOS‐2) have been developed and launched from 2014–2017, and this trend is set to continue with Sentinel‐1C/D, Gaofen‐3B/C, RADARSAT Constellation planned for launch during 2018–2025. This poses more challenges for correcting interferograms for atmospheric effects since the spatial‐temporal variations of tropospheric delay may dominate over large scales and completely mask the actual displacements due to tectonic or volcanic deformation. To overcome this, we have developed a generic interferometric synthetic aperture radar atmospheric correction model whose notable features comprise (i) global coverage, (ii) all‐weather, all‐time useability, (iii) correction maps available in near real time, and (iv) indicators to assess the correction performance and feasibility. The model integrates operational high‐resolution European Centre for Medium‐Range Weather Forecasts (ECMWF) data (0.125° grid, 137 vertical levels, and 6‐hr interval) and continuous GPS tropospheric delay estimates (every 5 min) using an iterative tropospheric decomposition model. The model's performance was tested using eight globally distributed Sentinel‐1 interferograms, encompassing both flat and mountainous topographies, midlatitude and near polar regions, and monsoon and oceanic climate systems, achieving a phase standard deviation and displacement root‐mean‐square (RMS) of ~1 cm against GPS over wide regions (250 by 250 km). Indicators describing the model's performance including (i) GPS network and ECMWF cross RMS, (ii) phase versus estimated atmospheric delay correlations, (iii) ECMWF time differences, and (iv) topography variations were developed to provide quality control for subsequent automatic processing and provide insights of the confidence level with which the generated atmospheric correction maps may be applied.