Snow Cover Mapping Using Polarization Fraction Variation With Temporal RADARSAT-2 C-Band Full-Polarimetric SAR Data Over the Indian Himalayas

Muhuri, Arnab; Manickam, Surendar; Bhattacharya, Avik; Snehmani,

doi:10.1109/jstars.2018.2817687

Cited by 35 publications

(30 citation statements)

References 70 publications

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“…The relationship between snowpack parameters and the amplitude of backscattering Snow wetness − [114] + [92,99,115] Snow grain size + [106,116] insignificant [117] Snow depth/thickness + [116,118] +coarse-grained snowpack [96] −fine-grained snowpack [96] − [117] Remote Sens. 2019, 11, 1456 6 of 44…”

Section: Backscattering Coefficient High Lowmentioning

confidence: 99%

“…Based on the observation that dry snow shows lower H and α values, they also derived the ratio of H and α to detect dry snow. Similar techniques were employed by [96,118,159,168,169], calculating the difference of available parameters or relying on machine learning (ML) classifications to detect snow cover. Venkataraman et al [170,171] even proposed a Radar Snow Index (RSI) to estimate the total SCE based on polarization fraction parameters.…”

Section: Wet and Dry Sce Detectionmentioning

confidence: 99%

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Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

et al. 2019

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The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

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Section: Backscattering Coefficient High Lowmentioning

confidence: 99%

Section: Wet and Dry Sce Detectionmentioning

confidence: 99%

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

et al. 2019

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“…The nominal spatial resolutions in this mode are 5.4 × 7.9 m in range and azimuth directions, respectively, for incidence angles between 18°and 49°. Both the ALOS-2/PALSAR-2 and RADARSAT-2 data sets have been widely explored for snow studies (Eshqi Molan et al, 2018;Muhuri et al, 2017;Muhuri et al, 2018;Surendar et al, 2017;Usami et al, 2016).…”

Section: 1029/2019wr025449mentioning

confidence: 99%

“…In Martini et al (2006) Landsat images were used for deriving the snow cover area. Muhuri et al (2017Muhuri et al ( , 2018 have also used Landsat-8 data for evaluation of their modeled snow cover area. The multispectral data sets used to develop the reference snow cover maps are defined in Table 1.…”

Section: 1029/2019wr025449mentioning

confidence: 99%

“…Thus, these methods are still under comprehensive development. Other constraints for such studies include the requirement of snow‐free data sets or multitemporal data sets (Martini et al, ; Muhuri et al, ; Muhuri et al, ; Nagler et al, ; Park et al, ), or ground truth in the case of supervised classification based techniques (Tsai et al, ). In Singh et al (), the thresholds for RSI for snow identification were determined from the knowledge of the other land cover in the study area.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

Varade

Singh

Dikshit

et al. 2020

Water Resources Research

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Recently, with the extensive availability of fully polarimetric synthetic aperture radar (SAR) data, methods that are simple and efficient, and involve lesser computation and data processing, are needed to be explored for snow cover mapping. This paper analyzes different polarimetric parameters such as entropy, anisotropy, and the mean scattering angle for the identification of snow cover area. We present a novel index for mapping snow cover based on the assessment of entropy (H) and anisotropy (A) using fully polarimetric SAR data and refer to it as the radar snow fraction (RSF). The RSF is proposed as an extension of the H(1‐A) metric by applying a sigmoidal function to this metric. The experiments to evaluate the applicability of the proposed RSF are carried out using fully polarimetric SAR data of L‐band ALOS‐2/PALSAR‐2 and C‐band RADARSAT‐2 data sets corresponding to different geographical locations in the Indian Himalayas. The developed snow cover maps from the proposed method were validated with respect to reference snow cover maps derived by thresholding the Normalized Differenced Snow Index developed from multispectral data (e.g., Landsat‐8 imagery). These maps were also statistically compared with those obtained from the conventional radar snow index, which is based on the polarization fraction. We determined a mean overall accuracy of 0.8 between the developed snow cover maps and the reference maps for the different data sets used for experiments. The results showed that, in general, the RSF outperformed the other polarimetric parameters for snow cover detection.

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Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation

Tao

2021

Intl Journal of Climatology

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Mountain snowpacks play important roles in water resource and ecological system. However, existing snow depth products show great uncertainties across the Tianshan Mountains, Central Asia. This study evaluated and compared four snow depth datasets over the Tianshan Mountains, including snow depth datasets from ERA5, ERA5‐Land, passive microwave, as well as a dynamically downscaled simulation by Weather Research and Forecasting (WRF) model. The snow depth was evaluated against in situ observations while the snow cover extent was evaluated by interactive multisensor snow and ice mapping system (IMS) snow cover. Furthermore, the snow‐related metrics, such as annual mean snow depth and snow cover days, were compared among the four datasets. The results showed that the spatial patterns of snow‐related metrics were relatively consistent among datasets, although discrepancies existed in magnitude. Additionally, the temporal variations in snow‐related metrics depended on the dataset employed. The simulated snow depth from WRF got the lowest bias in daily snow depth value against the in situ observations (mean bias = 0.12 cm, root mean square error = 2.08 cm), additionally, it had the best performance in classifying correct snow grids compared with IMS (probability of detection = 0.766). It also outperformed in annual mean snow depth and snow cover days by 47.2 and 13.3% compared with ERA5‐Land snow depth. This study highlights the confidence in characterizing both the spatial pattern and temporal variations of snow depth products based on WRF model simulation.

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Snow Cover Mapping Using Polarization Fraction Variation With Temporal RADARSAT-2 C-Band Full-Polarimetric SAR Data Over the Indian Himalayas

Cited by 35 publications

References 70 publications

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

Identification of Snow Using Fully Polarimetric SAR Data Based On Entropy and Anisotropy

Evaluation of snow depth and snow cover represented by multiple datasets over the Tianshan Mountains: Remote sensing, reanalysis, and simulation

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