Augmented iterative tropospheric decomposition strategy for GNSS-based zenith tropospheric delay map generation

Global Navigation Satellite System (GNSS) permanent networks, deployed for geodetic purposes, provide valuable information on atmospheric water vapor content. The interaction between GNSS signals and the troposphere affects the signal propagation velocity, introducing an observable extra-path or delay along the zenith direction above each station, known as the Zenith Tropospheric Delay (ZTD). ZTDs can be used to correct Synthetic Aperture Radar (SAR) observations, which are influenced by similar propagation delays. To achieve this, ZTD maps with the same spatial resolution as the SAR observed images must be generated. In some cases a direct spatial interpolation of the delays is performed, in some others a tomographic approach is applied to derive a three-dimensional refractivity grid and then integrated to derive maps of delay along the SAR signal line of sight. This paper introduces a two-step procedure, called Augmented Iterative Tropospheric Decomposition (AITD), which can be ascribed to direct spatial interpolation techniques. It is derived from the well-established Iterative Tropospheric Decomposition (ITD) strategy, implemented in the Generic Atmospheric Correction Online Service for InSAR (GACOS), to interpolate dense and regular ZTDs derived from Numerical Weather Prediction Models (NWPMs). The AITD is comparable to the original decomposition approach in terms of prediction error accuracy (the prediction error Root-Mean-Square (RMS) of the two strategies differs by 1 mm, corresponding to approximately 10% of the ITD RMS, as assessed by a leave-one-out validation). However, it allows for the mitigation of interpolation artifacts introduced by the original approach when applied to sparse and not regularly distributed data. The augmented procedure allows to reduce the standard deviation of SAR phase screens by 45% and is computationally more efficient than ITD. It halves the processing time for GNSS-derived ZTDs and is nearly 20 times faster when applied to NWPM-derived ZTDs. The procedure enables the generation of high temporal resolution time series of maps as an additional product of GNSS network data processing, giving useful insight on the water vapor distribution for meteorological purposes.