Seasonal variation of coherence in SAR interferograms in Kiruna, Northern Sweden

Baduge, Anura Wickramanayake Andra; Henschel, Michael D.; Hobbs, Stephen; Buehler, Stefan A.; Ekman, Jonas; Lehrbass, Brad

doi:10.1080/01431161.2014.915435

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…This is probably due to random phase drifts over time that cannot be estimated and corrected in a spaceborne system compared to a ground radar. Vegetation cover decreases the temporal coherence significantly at high frequencies [37]. More studies are needed to better model temporal coherence at different frequencies, different land types, and different environmental condition.…”

Section: Using Differential Interferometry To Estimate Swementioning

confidence: 99%

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

et al. 2020

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A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.

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Section: Using Differential Interferometry To Estimate Swementioning

confidence: 99%

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

et al. 2020

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“…This method was then applied to all data for the Kiruna site, with previously collected data re-analysed in the final deliverable. Additional research and development work is reported in Wickramanayake et al (2016) and Mäkitaavola et al (2016).…”

Section: Monitoring Resultsmentioning

confidence: 99%

InSAR as a practical tool to monitor and understand large-scale mining-induced ground deformations in a caving environment

Mäkitaavola¹,

Stöckel²,

Savilahti³

et al. 2018

Proceedings of the Fourth International Symposium on Block and Sublevel Caving

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The application of InSAR technology to monitor mining-induced deformations has been successfully applied at the Kiruna mine site for more than seven years. Ground deformations are caused by the use of large-scale sublevel cave mining to extract iron ore from the underground Kiirunavaara mine. More recently, InSAR monitoring has also been applied at the nearby LKAB Malmberget mine site, also mined using sublevel caving. In both cases, the induced ground deformations are affecting existing infrastructure, necessitating an urban transformation process to relocate surface infrastructure and/or residential areas. Control and follow-up of ground deformations has thus become vital, and InSAR monitoring has proven to play an ever-increasingly important role in this. The technique and its use at a high-latitude (snow-covered) site, and for LKAB's specific purposes, was initially investigated and verified during a five-year research project. A slightly expanded monitoring program is now in place in Kiruna, which supplements the standard GPS monitoring.At Malmberget, InSAR monitoring has enabled verification of the overall movement trends with respect to major shear structures in the area. Additionally, increased reliability in areas with poor GPS coverage has been achieved, as well as increased data coverage in periods between GPS measurement campaigns. Combined use of InSAR and GPS data has also helped to calibrate and constrain numerical models, as exemplified in this paper. Work is now ongoing to implement the use of InSAR to monitor the criterion for allowable ground deformations at both sites, as well as in integrated interpretation of caving mechanics and deformations.

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“…This is probably due to random phase drifts over time that cannot be estimated and corrected in a spaceborne system compared to a ground radar. Vegetation cover decreases the temporal coherence significantly at high frequencies (Baduge et al, 2016;Kellndorfer et al, 2022;Ruiz et al, 2022). A tower-based fully polarimetric InSAR studied the effect of air temperature, precipitation intensity, and wind on the temporal decorrelation at L-, S-, C-, and X-bands (Ruiz et al, 2022).…”

Section: Temporal Coherencementioning

confidence: 99%

Snow Water Equivalent Retrieval Over Idaho, Part A: Using Sentinel-1 Repeat-Pass Interferometry

Oveisgharan

Zinke

Keskinen

et al. 2023

Preprint

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Abstract. Snow Water Equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and~passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the optimal radar frequencies and temporal repeat intervals have not been available until recently. Interferometric Synthetic Aperture Radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of Sentinel-1 data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent measurements of snow depth and SWE. The Pearson correlation and RSME between retrieved SWE change observations and in situ stations measurements are 0.82, and 0.76 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows SWE error less than 2 cm for the 16 in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is highly correlated with LIDAR snow depth data with correlation of more than 0.5. Low temporal coherence is the main reason for degrading the performance of SWE retrieval using InSAR data. Low temporal coherence also causes the degradation of phase unwrapping algorithms.

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Seasonal variation of coherence in SAR interferograms in Kiruna, Northern Sweden

Cited by 12 publications

References 11 publications

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

InSAR as a practical tool to monitor and understand large-scale mining-induced ground deformations in a caving environment

Snow Water Equivalent Retrieval Over Idaho, Part A: Using Sentinel-1 Repeat-Pass Interferometry

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