Satellite-based InSAR (Interferometric Synthetic Aperture Radar) provides an effective way to measure large-scale land surface motions. Currently, the atmospheric phase delay is one of the most critical issues in InSAR deformation monitoring. Generic Atmospheric Correction Online Service (GACOS) is a free, globally available and easy-to-implement tool to generate high-resolution zenith total delay maps, which could be used for InSAR atmospheric delay correction. The mean velocity could then be estimated by stacking multiple GACOS-corrected interferograms. We applied the proposed GACOS-corrected InSAR stacking method in the North China Plain and analysed its performance. Within the 549 interferograms, more than 85 % gained positive correction performances. The correlation between the phase-dZTD indicator and the performance reached 0.89, demonstrating a significant relationship. Deformation maps revealed by InSAR stacking with and without GACOS corrections showed that GACOS could mainly remove the topography-related and long wavelength signals.
The successful operation of the European Space Agency's (ESA) Sentinel-1
satellites provide unprecedented possibilities and convenience for
large-scale land surface deformation measurements. Ground motion monitoring
using InSAR are extended from local, regional practices to full, nationwide
scale applications. In recent years, the implementations of researches or
projects covering a whole country, such as Italy (Costantini et al.,
2017), Germany (Haghighi and Motagh, 2017), Norway (
Although InSAR has been proven successful in many existing cases, there are still inherent limitations in the technique. At this stage, the atmospheric phase delay, mainly affected by the differences in propagation paths through the troposphere, is one of the most critical issues in large-scale InSAR deformation monitoring.
The second part of this paper briefly introduces InSAR atmospheric effects and the existing methods for mitigation. Section 3 describes the data processing strategy of the proposed GACOS-corrected InSAR stacking method. Study area, satellite data used, and results are presented in Sect. 4, followed by the conclusions in Sect. 5.
The propagation paths of the microwave signals of the SAR sensors on the satellite are affected during the 2-pass through the Earth's atmosphere, expressed as the changes in the transit time, which is always known as the atmospheric effects of InSAR (Li et al., 2005). The tropospheric effects are mainly related to the variations of the water vapour in the troposphere, as well as the temperature and pressure changes at different SAR image acquisition time. Generally, the atmospheric effect could result in up to 15–20 cm errors in the interferograms, which may interfere with or even make the signals of interest indistinguishable. Researchers have developed numerous means to mitigate atmospheric effects. According to the data used, there are mainly two types of methods: atmospheric corrections with or without external data.
No external data employed means there are no other atmospheric parameter measurements, such as water vapour and temperature, involved in the processing. This kind of phase-based corrections, such as phase-elevation estimate (Bekaert et al., 2015) or PSI (Ferretti et al., 2001), are always based on certain assumptions. As its name suggests, the major benefit of this type of methods is that external data are not needed, and it is straightforward and easy-to-implement. However, the disadvantages are obvious: (i) this method may have the risk that the signal of interest is removed, (ii) in some scenarios, it is difficult or impossible to evaluate the performance of atmospheric signal removal quantitatively.
Data processing flowchart of GACOS-corrected InSAR stacking.
The external data used in InSAR atmospheric correction mainly includes three
types of data, i.e., satellite-based spectrometer observations, ground
observations and numerical meteorological models. MEdium Resolution Imaging
Spectrometer (MERIS) (Li et al., 2012) and Moderate
Resolution Imaging Spectroradiometer (MODIS) offer near-IR water vapour
products at a fine resolution (
Although there are successful cases, every type of correction methods has limitations and cannot always promise success (Murray et al., 2019). Moreover, the processing of these external data further increases the complexity of InSAR data processing procedure. The development of Generic Atmospheric Correction Online Service for InSAR (GACOS), to some extent, has solved the above problems.
Overview of the study area with topography as the background map.
Spatial and temporal baselines of the interferograms.
Launched in June 2017, GACOS has distributed more than 60 000 tasks all over
the world. Together with DEM data, it utilises atmospheric model High
RESolution 10-day forecast (HRES) datasets, which is ECMWF's
highest-resolution model of up to
The averaging of multiple interferograms (stacking) is the simplest attempt
to remove the influences of errors such as the atmosphere in time-series
InSAR analysis. In this model, the signal of interest, i.e., deformation in the
interferograms, is assumed to have a systematic pattern, and the atmospheric
noise is random. The method of least squares could significantly increase
the signal-to-noise ratio by reducing the random noises (Wright et
al., 2001). Mean velocity map is the most intuitive way to show the
characteristics of deformation. A constant rate of each pixel is estimated
by
As shown in Fig. 1, we proposed a new method estimating the deformation rate by InSAR stacking with GACOS-corrected interferometric phases. The interferograms generation process is similar to the traditional small baseline subset method. After phase unwrapping, the phases are corrected pair by pair using GACOS differential ZTDs (dZTDs). We can employ performance indicators to evaluate the effectiveness of the corrections and decide whether to apply the correction to one specific interferogram or not. Finally, the mean velocities are estimated using the updated phases.
As China's largest alluvial plain, the North China Plain covers the area of
32–40
GACOS performance statistics from 549 interferograms.
GACOS ZTD difference
We explore the study area with SAR images acquired on 66 days between
October 2016 and March 2019, by Sentinel-1b. SAR imaging time is 22:04 UTC.
Figure 2 demonstrates the location and topography of the study area. The red
rectangle region involves 55 bursts in 3 sub-swaths. We multi-look the
interferograms by the factor of 40 (range) and 8 (azimuth), which refers to
the resolution of
The phase standard deviations of interferograms before and after correction
are always used for assessment of the performance of atmospheric signal
removal. Considering the existing deformation signals in the interferograms,
we mask out the fast deforming areas with a mean velocity larger than 30 mm yr
Two InSAR atmospheric correction examples are shown in Fig. 5. The first
column (Fig. 5a–c) shows the interferogram generated by SAR images
dated on 5 and 17 October 2016 with a spatial baseline of
The correlation between the interferometric phases and dZTDs could be used as an indicator for the applicability of GACOS (Yu et al., 2018). Here we investigate the correlation between the indicator and GACOS performance. Figure 6 shows that the mean correlation (indicator) of the 549 interferograms is 0.71, and the mean GACOS performance is 27.29 %. The correlation between them is 0.89, determining that the performance has a significant relationship with the indicator. Statistics from the 471 interferograms with positive performances demonstrate that there is a nearly linear relationship between the indicator and the performance: the higher the phase-dZTD correlations, the more positive effect of the GACOS corrections.
Correlation analysis of the phase-dZTD indicator and GACOS performance.
The LOS deformation maps are revealed by stacking from the original and corrected interferograms, as shown in Fig. 7. We can see topography-related and long wavelength signals exist in the original deformation map (Fig. 7a), while they are largely removed by GACOS (Fig. 7b).
In Fig. 7a, the deformation pattern is obscured by the atmospheric effects, which leads to a misunderstanding of the land surface motions in the study area. GACOS solved the issue in a simple and effective way. The development of the new generation satellites as Sentinel-1 with rapid revisiting period (12 to 6 days) in the big data era, increases the attention on algorithm efficiency and estimation efficiency in the data processing practice of large-scale ground deformation general surveys. GACOS-corrected InSAR stacking, as a robust method which is straightforward and easy-to-implement, provide an effective way for general surveys of land surface deformation.
Deformation revealed by InSAR stacking without
As an InSAR atmospheric correction method with external data, GACOS has the
advantages of globally available, near real-time and easy to implement. In
this paper, we proposed the GACOS-corrected InSAR stacking method for
general surveys of the land surface deformation. The main conclusions are as
follows:
The standard deviations of 471 interferograms decreased after GACOS
corrections, i.e., more than 85 % interferograms, in this case, got positive
performances. The correlation between the phase-dZTD indicator and the performance was
analysed, and the correlation of 0.89 demonstrated a significant
relationship. Deformation was revealed by InSAR stacking method from the original and
corrected interferograms, showing that GACOS largely removed the
topography-related and long wavelength signals.
Sentinel-1 SAR data used in this study are from European Space Agency (ESA), downloaded through Copernicus Open Access Hub at
RX conceived and designed the experiments. RX and CY performed the experiments and analyzed the results. RX wrote the manuscript and the whole team provided suggestions and reviewed the manuscript.
The authors declare that they have no conflict of interest.
This article is part of the special issue “TISOLS: the Tenth International Symposium On Land Subsidence – living with subsidence”. It is a result of the Tenth International Symposium on Land Subsidence, Delft, the Netherlands, 17–21 May 2021.
The authors thank the reviewers and editors for their helpful comments.
This research has been supported by the National Natural Science Foundation of China (grant no. 41804005), the Fundamental Research Funds for the Central Universities (grant no. 2019B17414), and the Natural Science Foundation of Jiangsu Province (grant no. BK20170869). Ruya Xiao received support from the Chinese Scholarship Council (ref. 201806715019).