Assessment of InSAR tropospheric signal correction methods

Haji-Aghajany, Saeid; Amerian, Yazdan

doi:10.1117/1.jrs.14.044503

Cited by 13 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although we attempt to correct for signals caused by tropospheric delays, no currently available method can remove all of this noise, especially delays arising from the turbulent component of the atmosphere [42][43][44]. Previous studies have shown that in general, weather-model derived corrections can reduce the noise by 2-16% [45] resulting in an RMSE of around 2-3 cm [42] in individual interferograms.…”

Section: Discussionmentioning

confidence: 99%

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

et al. 2021

View full text Add to dashboard Cite

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

show abstract

Section: Discussionmentioning

confidence: 99%

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the first step of processing, the subsidence points need to be carefully identified. Given that the weather conditions of the study area may result in a change in the subsidence signal position obtained from InSAR, a precise method should be used to eliminate the tropospheric effect in radar results and to obtain an accurate diagnosis of signals [14,15]. In the following, a detailed method to correct the tropospheric effect of InSAR measurements is described.…”

Section: Background and Methodologymentioning

confidence: 99%

Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

2023

View full text Add to dashboard Cite

This study aims to investigate how changes in meteorological indicators affect groundwater resources, and hence to predict groundwater levels using these indicators, particularly in regions experiencing drought and subsidence. Precipitation, temperature, evapotranspiration and precipitable water vapor (PWV) are important meteorological parameters to predict groundwater levels. Two subsidence areas with different weather conditions were selected to conduct a comprehensive study on the effect of temperature and precipitation on groundwater level changes. The correct locations of the two subsidence areas were determined by analyzing Interferometric Synthetic Aperture Radar (InSAR) images of Sentinel-1A using the small baseline subset algorithm. The interferograms were processed to correct tropospheric effects using the advanced integration method. Specifying the exact locations of the two areas, the meteorological parameters were downscaled using the Statistical DownScaling Model (SDSM), synoptic observations, meteorological data, and the General Circulation Model (GCM). An Artificial Neural Network (ANN) was then employed to predict the groundwater level changes as a function of meteorological data, including Global Positioning System (GPS)-based PWV and the evapotranspiration index. The trained ANN, along with the downscaled meteorological indicators, was used to predict groundwater level changes over two time periods. In the first period, the prediction was performed over the current years to investigate the performance of the method using the available data, whereas in the second period, the prediction was performed for the coming years, up until 2030. The results confirmed the high performance of the prediction algorithm, and the importance of including PWV and evapotranspiration in groundwater level predictions. The Pearson correlation coefficient was used to check the relationship between groundwater level changes and meteorological variables. The statistical significance of these coefficients was tested at the significance level α=0.05. In more than 80% of the cases, the correlation coefficients were statistically significant, reaching more than 0.70 in some of the months. It is also observed that an increase in the depth of groundwater level has an obvious relationship with an increase in temperature and a decrease in rainfall.

show abstract

“…As to Pan'an New City, the correlation is always higher in urban areas. The latter relates to the interaction between radar waves and the atmosphere including ionospheric [27], tropospheric [28][29][30][31], etc. Even though there are ways to make them weaker, it is hard to eliminate them.…”

Section: Technical Principle Of Sbas-insarmentioning

confidence: 99%

Goaf Site Stability Detection in the Overlap Area of Coal Mining Subsidence and Urban Construction

Dai,

Li,

Liu

et al. 2024

Advances in Civil Engineering

View full text Add to dashboard Cite

The contradiction between coal mining and urban construction in coal resource-based cities is prominent, which greatly limits the sustainable development of these cities. Pan’an New City is a key mining-induced subsidence area in Xuzhou City, which presents significant challenges to the construction of the new city. Therefore, in order to ensure the safe construction of Pan’an New City, the residual deformation and stability of the goaf sites must be monitored and evaluated. Under such background, based on the measured leveling data of the mining-induced surface deformation in a coal mine near Pan’an New City, this paper first analyzed the accuracy of InSAR monitoring of surface deformation in coal mining subsidence area by SBAS-InSAR technology. Then, the SBAS-InSAR technology was used to monitor the surface subsidence rate and cumulative subsidence in the coal mining subsidence area of Pan’an New City, based on the 29 scene SAR data during Dec. 2020 and Jan. 2022. The results showed that the goaf site in the north and northwest of Pan’an New City is unstable, while the other areas are stable. Finally, according to the monitoring results, the suggestions have been put forward for the construction of Pan’an New Town on the goaf site. The research results have important theoretical and practical significance for the reuse of goaf sites in Pan’an New City and similar areas in Xuzhou.

show abstract

Assessment of InSAR tropospheric signal correction methods

Cited by 13 publications

References 31 publications

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Offline-Online Change Detection for Sentinel-1 InSAR Time Series

Impact of Climate Change Parameters on Groundwater Level: Implications for Two Subsidence Regions in Iran Using Geodetic Observations and Artificial Neural Networks (ANN)

Goaf Site Stability Detection in the Overlap Area of Coal Mining Subsidence and Urban Construction

Contact Info

Product

Resources

About