Estimation of snow water equivalent from L-band radar interferometry: simulation and experiment

Dagurov, P. N.; Чимитдоржиев, Т. Н.; Дмитриев, А. В.; Dobrynin, S. I.

doi:10.1080/01431161.2020.1798551

Cited by 14 publications

(10 citation statements)

References 51 publications

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“…Now, the only option is L-band ALOS-2 PALSAR-2 radar interferometric data for the winter period with a time baseline of about one year (see Table 1). Herein, we assume that the thickness of the snow cover is the same in different years and does not create an additional phase shift [19]. In the first stage (Figure 1), we need to detect a PHM against the background of a multiple chaotic terrain changes of various spatial scales.…”

Section: Phm Detection and Data Usedmentioning

confidence: 99%

“…Now, the only option is L-band ALOS-2 PALSAR-2 radar interferometric data for the winter period with a time baseline of about one year (see Table 1). Herein, we assume that the thickness of the snow cover is the same in different years and does not create an additional phase shift [19]. ALOS-2 PALSAR-2 images at ascending orbit in FBD (Fine Beam Dual) mode were processed using Sarmap SARScape © software.…”

Section: Phm Detection and Data Usedmentioning

confidence: 99%

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A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia

2023

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Climate change in the Arctic region is more significant than in other parts of our planet. One of the manifestations of these changes is crater creation with blowouts of a gas, ice and frozen soil mixture. In this context, dynamics studies of long-term heaving mounds that turn into craters as a result are relevant. A workflow for detecting and assessing anomalous dynamics of heaving mounds in the Arctic regions is proposed. Areas with anomalous increase of ALOS-2 PALSAR-2 synthetic aperture radar (SAR) backscattering intensity are detected in the first stage. These increases take place due to sudden changes in local terrain slopes when the scattering surface (mound slope) turns toward the radar. Radar backscattering intensity also rises due to depolarization at newly formed frost cracks. Validation of the detected anomaly is carried out at the second stage through a comparison of multi-temporal digital elevation models obtained from bistatic radar interferometry TerraSAR-X/TanDEM-X data. At the final stage, the deformations are assessed within the detected areas using differential SAR interferometry (DInSAR) technique by ALOS-2 PALSAR-2 data. The magnitude of the heaving along the line of sight (LOS) was 22–24 cm in the period from January 2019 to January 2020. In general, effectiveness for detecting the perennial heaving mounds and the rate assessment of their increase were demonstrated in the suggested workflow.

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Section: Phm Detection and Data Usedmentioning

confidence: 99%

“…Now, the only option is L-band ALOS-2 PALSAR-2 radar interferometric data for the winter period with a time baseline of about one year (see Table 1). Herein, we assume that the thickness of the snow cover is the same in different years and does not create an additional phase shift [19]. ALOS-2 PALSAR-2 images at ascending orbit in FBD (Fine Beam Dual) mode were processed using Sarmap SARScape © software.…”

Section: Phm Detection and Data Usedmentioning

confidence: 99%

A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia

2023

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“…To assess possible landslide reactivation using multitemporal C-band Sentinel-1B data we used PSI and SBAS-InSAR methods. To exclude a possible impact of snow on differential interferometry results [73], SAR data for 3 May-6 October 2019 (14 scenes), 9 May-12 October 2020 (13 scenes) and 4 May-25 September 2021 (12 scenes) were used; 39 co-polarized (VV) images in total.…”

Section: Psi and Sbas-insarmentioning

confidence: 99%

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

et al. 2021

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In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.

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“…Active microwave sensors provide high resolution and global coverage. SWE can be retrieved using active microwave remote sensing from space (Cui et al, 2016;Leinss et al, 2014Leinss et al, , 2015Oveisgharan and Zebker, 2007;Lemmetyinen et al, 2018;Yueh et al, 2017Yueh et al, , 2021Conde et al, 2019;Liu et al, 2017;Eppler et al, 2022;Dagurova et al, 2020;Nagler et al, 2022;Engen et al, 2004;Larsen et al, 2005;Lievens et al, 2019). Backscattered power from active sensors can be used to estimate SWE (Rott et al, 2010;Ulaby and Stiles, 1980;Cui et al, 2016;Nghiem and Tsai, 2001;Lievens et al, 2019).…”

Section: Introductionmentioning

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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Estimation of snow water equivalent from L-band radar interferometry: simulation and experiment

Cited by 14 publications

References 51 publications

A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia

A Step-Wise Workflow for SAR Remote Sensing of Perennial Heaving Mound/Crater on the Yamal Peninsula, Western Siberia

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

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

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