Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

Cao, Yunmeng; Li, Zhi Wei; Wei, Jianchao; Hu, Jun; Duan, Meng; Feng, Guangcai

doi:10.1007/s00190-017-1055-5

Cited by 43 publications

(37 citation statements)

References 35 publications

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“…The OTL effect and other large-scale signals, such as the solid Earth tidal (SET) displacement and the atmospheric delay error related to topography and orbital error, will be mixed in the differential interferogram [16][17][18][19]. The wavelength of the SET displacement is one or two orders larger than the OTL effect, and its effect in an Advanced Synthetic Aperture Radar (ASAR) interferogram can be easily corrected using a SET correction model [20].…”

Section: Introductionmentioning

confidence: 99%

The Extraction of Ocean Tidal Loading from ASAR Differential Interferograms

Peng

Wang

Cao

2020

Sensors

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The spatiotemporal crustal non-tectonic deformation caused by ocean tidal loading (OTL) can reach the centimeters scale in coastal land areas. The temporal variation of the site OTL displacements can be estimated by the global positioning system (GPS) technique, but its spatial variation needs to be further determined. In this paper, in order to analyze the spatial characteristics of the OTL displacements, we propose a multi-scale decomposition method based on signal spatial characteristics to derive the OTL displacements from differential interferometric synthetic aperture radar (D-InSAR) measurements. The method was tested using long-term advanced synthetic aperture radar (ASAR) data and GPS reference site data from the Los Angeles Basin in the United States, and we compared the results with the FES2014b tide model. The experimental results showed that the spatial function of the OTL displacements in an ASAR image can be represented as a higher-order polynomial function, and the spatial trends of the OTL displacements determined by the InSAR and the GPS techniques are basically consistent with the FES2014b tide model. The root-mean-square errors of the differences between the spatial OTL displacements of these two methods and the FES2014b tide model are less than 0.8 mm. The results indicate that the OTL displacement extracted from InSAR data can accurately reflect the spatial characteristics of the OTL effect, which will help to improve the spatial resolution and accuracy of the OTL displacement in coastal areas.

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Section: Introductionmentioning

confidence: 99%

The Extraction of Ocean Tidal Loading from ASAR Differential Interferograms

Peng

Wang

Cao

2020

Sensors

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“…HE Interferometric synthetic aperture radar (InSAR) is a powerful tool for mapping the Earth's ground displacements, and the Sentinel-1 measurements in coastal areas usually contain tectonic deformation, atmospheric delay, the ocean tidal loading (OTL) effect, solid Earth tidal (SET) effect, orbital error, residual topographic phase error, and decorrelation noise [1][2][3]. The OTL effect is the response of the solid Earth to ocean mass redistribution due to the gravitational force changes, its gradient nonlinearly decreases from cm/100km scale in the coastal area to mm/100km scale in the area 200 km away from the coast [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Ocean Tidal Loading in InSAR Measurements Determined by Kinematic PPP Solutions of a Regional GPS Network

Peng

Wang

Zhan

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The coastal crustal deformation caused by ocean tidal loading (OTL) varies spatially and temporally, and this spatiotemporal variation in satellite-based interferometric synthetic aperture radar (InSAR) measurements needs to be determined. In this paper, we propose a spatiotemporal modeling method to estimate the OTL displacements in InSAR measurements using the kinematic precise point positioning (PPP) solutions of a regional GPS network. We tested the method through an experiment using 25 Sentinel-1B images and long-term observations of 172 GPS reference sites from Southern California. The experimental results suggest that there are significant OTL and solid Earth tide effects in the differential InSAR interferogram, which is greater than 40 mm. We find that the spatial characteristics of OTL variations can be expressed as a high-order polynomial in the two variables of latitude and longitude, and the spatiotemporally modeled PPP tidal estimates of the high-density GPS sites can provide high precision OTL correction for all the pixels in the interferogram. In the last part of the study, we show that the spatial large-scale signals in the differential interferograms of Sentinel-1B data are mainly atmospheric delay, solid Earth tidal and OTL effect, and demonstrate the importance of the tidal correction in the InSAR measurements.

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“…The major source of noise in the InSAR time series is the combination of tropospheric delay, which comprise vertical stratification and turbulent components [2]. The vertical stratification component can be estimated by its relation with height (e.g., [9]), but the turbulent component is variable in flat areas, so more difficult to determine [10].…”

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confidence: 99%

“…Spatio-temporal filtering (STF) is used by [13] in the Stanford Method for Persistent Scatters (StaMPS, see also [14]) on post-processed PS to reduce temporal noise in the time series (mostly time-and space-variable turbulent tropospheric noise, cf. [10]), and for spatial noise relative to other PSs. Like all filters, this can be set at different temporal and spatial scales, which will tend to remove noise, but may also remove parts of the sought-after deformation signal, particularly where the noise and deformation display similar characteristics in time and/or space [15].…”

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confidence: 99%

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A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

Khaki

Filmer

Featherstone

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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This study proposes an alternative filtering tech-1 nique to improve interferometric synthetic aperture radar (In-2 SAR) time series by reducing residual noise while retaining the 3 ground deformation signal. To this end, for the first time, a 4 data-driven approach is introduced, which is based on Takens's 5 method within the sequential Monte Carlo framework, allowing 6 for a model-free approach to filter noisy data. Both Kalman-7 based and particle filters are applied within this framework 8 to investigate their impact on retrieving the signals. More 9 specifically, particle filter (PF) and particle smoother (PaSm; 10 to avoid confusion with persistent scatterers PS) are tested for 11 their ability to deal with non-Gaussian noise. A synthetic test 12 based on simulated InSAR time series, as well as a real test, are 13 designed to investigate the capability of the proposed approach 14 compared with spatio-temporal filtering of InSAR time series.

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Stochastic modeling for time series InSAR: with emphasis on atmospheric effects

Cited by 43 publications

References 35 publications

The Extraction of Ocean Tidal Loading from ASAR Differential Interferograms

The Extraction of Ocean Tidal Loading from ASAR Differential Interferograms

Spatiotemporal Ocean Tidal Loading in InSAR Measurements Determined by Kinematic PPP Solutions of a Regional GPS Network

A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

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