Improving tropospheric corrections on large-scale Sentinel-1 interferograms 
using a machine learning approach for integration with GNSS-derived zenith 
total delay (ZTD)

Shamshiri, Roghayeh; Motagh, M.; Nahavandchi, Hossein; Haghshenas Haghighi, Mahmud; Hoseini, Mostafa

doi:10.1016/j.rse.2019.111608

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Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD)

Authors

/persons/resource/roghayeh

Shamshiri, Roghayeh
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/motagh

Motagh, M.
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Nahavandchi, Hossein
External Organizations;

/persons/resource/mahmud

Haghshenas Haghighi, Mahmud
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

/persons/resource/mostafa

Hoseini, Mostafa
0 Pre-GFZ, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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Citation

Shamshiri, R., Motagh, M., Nahavandchi, H., Haghshenas Haghighi, M., Hoseini, M. (2020): Improving tropospheric corrections on large-scale Sentinel-1 interferograms using a machine learning approach for integration with GNSS-derived zenith total delay (ZTD). - Remote Sensing of Environment, 239, 111608.
https://doi.org/10.1016/j.rse.2019.111608

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5000010

Abstract

Sentinel-1 mission with its wide spatial coverage (250 km), short revisit time (6 days), and rapid data dissemination opened new perspectives for large-scale interferometric synthetic aperture radar (InSAR) analysis. However, the spatiotemporal changes in troposphere limits the accuracy of InSAR measurements for operational deformation monitoring at a wide scale. Due to the coarse node spacing of the tropospheric models, like ERA-Interim and other external data like Global Navigation Satellite System (GNSS), the interpolation techniques are not able to well replicate the localized and turbulent tropospheric effects. In this study, we propose a new technique based on machine learning (ML) Gaussian processes (GP) regression approach using the combination of small-baseline interferograms and GNSS derived zenith total delay (ZTD) values to mitigate phase delay caused by troposphere in interferometric observations. By applying the ML technique over 12 Sentinel-1 images acquired between May–October 2016 along a track over Norway, the root mean square error (RMSE) reduces on average by 83% compared to 50% reduction obtained by using ERA-Interim model.