Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

Yang, Jinming; Li, Chengzhi

doi:10.1007/s12517-021-06699-y

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…The repeated-pass InSAR uses the phase of SAR data before and after snowfall to retrieve snow depth, which may suffer from temporal decorrelation, but the repeated-pass InSAR can obtain the phase difference information from before and after snowfall easily, which is the wider application in dry snow depth retrieval [18]. In addition, many studies have retrieved SD using differential interferometric synthetic aperture radar (D-InSAR) methods [20], [21]. For instance, the D-InSAR phases in the VV and VH polarization were corrected using snow-cover images, and this approach can obtain relatively accurate SD retrieval results [22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

Qiao,

Zhang,

et al. 2024

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

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Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.

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Section: Introductionmentioning

confidence: 99%

A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

Qiao,

Zhang,

et al. 2024

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

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“…Differential SAR Interferometry (D-InSAR) uses the geometric correlation between the slant range difference generated by microwave penetration through the snow layer and the interferometric phase of the snow layer to acquire snow-related information [23,24]. Yang and Li [25] assimilated snow depth from D-InSAR data using an Ensemble Kalman Filter. Lievens et al proposed a physical model to estimate snow depth using SAR images, employing backscattering σ0 to estimate snow depth in Northern Hemisphere mountain ranges, demonstrating a strong correlation between the σV H/σVV ratio and snow depth [26].…”

Section: Introductionmentioning

confidence: 99%

MFPANet: Multi-Scale Feature Perception and Aggregation Network for High-Resolution Snow Depth Estimation

Zhao,

Chen,

Shahzad

et al. 2024

Remote Sensing

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Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.

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“…The impact of an EnKF-based assimilation of both ground-based SWE observations and snowfall and snowmelt rates on distributed SWE estimates was analyzed in Magnusson et al (2014). More recently, three kinds of snow depth data which included the D-InSAR data retrieved from the remote sensing images, the automatically measured data using ultrasonic snow depth detectors, and the manually measured data were assimilated based on ensemble Kalman filter, and the results demonstrated that the assimilated snow depth data were spatiotemporally consecutive and integrated (Yang and Li, 2021).…”

Section: Introductionmentioning

confidence: 99%

Investigating the performance of Genetic Particle Filter in snow data assimilation across snow climates

You

Huang²,

Hou³

et al. 2022

Preprint

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Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.

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Assimilation of D-InSAR snow depth data by an ensemble Kalman filter

Cited by 8 publications

References 24 publications

A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

MFPANet: Multi-Scale Feature Perception and Aggregation Network for High-Resolution Snow Depth Estimation

Investigating the performance of Genetic Particle Filter in snow data assimilation across snow climates

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