10:45 AM - 12:15 PM
[STT39-P11] Progress of developing InSAR neutral atmospheric delay correction model using GNSS and global atmospheric model
Keywords:InSAR, atmospheric delay correction, GNSS, Global atmospheric model
The delay effect due to Earth's atmosphere is one of the most problematic error sources in InSAR and researches to correct it have been continued since the end of 1990s. In Kinoshita (2022, GSJ 138th symposium), the author reported a progress of developing a new correction model that uses a global atmospheric model output in addition to GNSS atmospheric observation data to achieve more accurate and versatile delay correction. However, the target area of the North Anatria Fault in Turkey, where Kinoshita (2022) selected, was not a prefferred area because available GNSS data were very few after 2016, causing insignificant assessment. In this study I added a new target area, where significant number of GNSS stations are available and a large number of Sentinel-1 SAR data can be used, to investigate potentials and limitations of a new delay correction model.
As for InSAR processing, I used 120 Sentinel-1 SLC images acquired around Reno, US, from 2019 to 2022, and then performed interferometric processing using ISCE ver.2.5.2, resulting in 650 interferograms. Most of InSAR pairs have temporal baselines lower than 2 months or just 1 year, whose setting is often used for the SAR time series analysis. Although a part of interferograms contained unwrapping errors, I will tackle with it in near future. As for GNSS data, I used 5-minutes PPP processing outputs processed and published by Nevada Geodetic Laboratory (NGL) in University of Nevada, Reno, and I used ERA5 global atmospheric model outputs. To include ERA5 information into GNSS-based delay correction model, I estimated zenith total delays (ZTDs) from ERA5 atmospheric variables and used them as inputs of the GNSS-based correction model. Assuming that the GNSS observation data would be more close to the real state of the atmosphere than ERA5, I manually set a larger weight to the GNSS data when estimating a InSAR delay amount.
Although the result shown here is a tentative result, the proposed delay correction showed significant correction performance as well as the GNSS-based correction by Kinoshita (2021, IEEE). The average phase standard deviation calculated from 230 interferograms was 14.59 mm, which decreased to 10.41 mm by the GNNN-based correction and to 10.37 mm by the proposed correction. Both models successfully mitigated approximately one third of phase variations. In the presentation, I will show the latest progress of this research.
ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grant Number JP21K14006.
As for InSAR processing, I used 120 Sentinel-1 SLC images acquired around Reno, US, from 2019 to 2022, and then performed interferometric processing using ISCE ver.2.5.2, resulting in 650 interferograms. Most of InSAR pairs have temporal baselines lower than 2 months or just 1 year, whose setting is often used for the SAR time series analysis. Although a part of interferograms contained unwrapping errors, I will tackle with it in near future. As for GNSS data, I used 5-minutes PPP processing outputs processed and published by Nevada Geodetic Laboratory (NGL) in University of Nevada, Reno, and I used ERA5 global atmospheric model outputs. To include ERA5 information into GNSS-based delay correction model, I estimated zenith total delays (ZTDs) from ERA5 atmospheric variables and used them as inputs of the GNSS-based correction model. Assuming that the GNSS observation data would be more close to the real state of the atmosphere than ERA5, I manually set a larger weight to the GNSS data when estimating a InSAR delay amount.
Although the result shown here is a tentative result, the proposed delay correction showed significant correction performance as well as the GNSS-based correction by Kinoshita (2021, IEEE). The average phase standard deviation calculated from 230 interferograms was 14.59 mm, which decreased to 10.41 mm by the GNNN-based correction and to 10.37 mm by the proposed correction. Both models successfully mitigated approximately one third of phase variations. In the presentation, I will show the latest progress of this research.
ACKNOWLEDGEMENT
This work was supported by JSPS KAKENHI Grant Number JP21K14006.