Estimation of Snow Cover Area, Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Thakur, Praveen K.; Aggarwal, Swati; G, Arun; Sood, Sahil; Kumar, Anil; Mani, Sneh; Dobhal, D. P.

doi:10.1007/s12524-016-0609-y

Cited by 40 publications

(30 citation statements)

References 43 publications

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“…The in-situ SSDs along with the corresponding snow densities and SSWEs are provided in Table 3.2. Apart from this, a forest mask used in previous studies involving this watershed area (Thakur et al, 2012(Thakur et al, , 2017 has been obtained from the Water Resources Department (WRD), Indian Institute of Remote Sensing (IIRS).…”

Section: Field Visitmentioning

confidence: 99%

“…Snow depth (SD) and snow water equivalent (SWE) are two of the most important physical properties of snow and are extensively used in hydrological models that relate to snowmelt runoff and snow avalanche predictions (Thakur et al, 2017). While snow depth or snow height refers to the distance of the ground to the snow surface, SWE quantifies the amount of water present in a snowpack (layered snow formed by accumulation over time).…”

Section: Introductionmentioning

confidence: 99%

“…PolSAR based algorithms which work on the polarimetric backscatter signal have been widely adopted for various snow related applications such as the classification of dry and wet snow, measuring snow wetness and snow density (Singh et al, 2017;Snehmani et al, 2010;Thakur et al, 2012Thakur et al, , 2017Usami et al, 2016). Leinss et al (2014) introduced the use of spaceborne PolSAR for snow height determination, wherein the relationship between the copolar phase difference (CPD) and fresh snow depth is quantitatively analysed by deriving a theoretical model.…”

Section: Introductionmentioning

confidence: 99%

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Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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Snow depth (SD) and Snow Water Equivalent (SWE) constitute essential physical properties of snow and find extensive usage in the hydrological modelling domain. However, the prominent impact of the hydrometeorological conditions and difficult terrain conditions inhibit accurate measurement of the SD and SWE— an ongoing research problem in the cryosphere paradigm. In this context, spaceborne synthetic aperture radar (SAR) systems benefit from global coverage at sufficiently high spatial and temporal resolutions. Still, existing polarimetric and interferometric SAR techniques are susceptible to high volume scattering resulting from the increased snow grain sizes due to the standing (or old) snow formation driven by the temperature induced snow metamorphosis process. Hence, to model this volume decorrelation, the polarimetric SAR interferometry (Pol-InSAR) technique can be effectively applied. In this work, the standing snow depth (SSD) and its corresponding standing snow water equivalent (SSWE) are estimated using the single-baseline Pol-InSAR based hybrid Digital Elevation Model (DEM) differencing and coherence amplitude inversion model. To achieve this, six TerraSAR-X, TanDEM-X Coregistered Single look Slant range Complex (CoSSC) bistatic quad-pol acquisitions between December 2015 and January 2016 over Dhundi (situated in the Beas watershed, northwestern Himalayas, India) are used. Due to the associated problems of model parameter tuning, complex topographical conditions, and limited ground-truth measurements, appropriate sensitivity analyses have been carried out for the parameter optimisation. Furthermore, the uncertainty sources are identified by performing a summer (June 8, 2017) and wintertime (January 8, 2016) comparative analysis of the study area which quantitatively highlights the changes in the percentages of the surface and volume scatterings. Evidently, the improved model displays sufficiently high overall SSD accuracy with coefficient of determination (R^2) ≈ 0.96, Mean Absolute Error (MAE) ≈ 1.61 cm, and Root Mean Square Error (RMSE) ≈ 2.16 cm. Additionally, the respective SSWEs have been calculated by assuming a fixed snow density for each epoch wherein the overall error metrics are R^2 ≈ 0.71, MAE ≈ 5.19 mm, and RMSE ≈ 6.84 mm. Therefore, this research successfully demonstrates the practicability of the improved Pol-InSAR model for SD estimation over rugged terrains.

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Section: Field Visitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Majumdar¹,

Thakur²,

Chang³

et al. 2019

Preprint

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“…By applying joint inversion using multispectral and hyperspectral data, the snowpack roughness can also be extracted [30]. Using image processing techniques, the extracted snow cover and glaciers or a fine classification of different snow cover types could also reflect the physical characteristics of the snowpack [31][32][33].Indirectly, other external parameters also provide indicators for SAP, such as surface temperature of snow cover, snowpack duration, cloudiness, precipitation, solar zenith angle, and elevation [34][35][36][37]. Many researches have explored using correlation analysis, sensitivity and response models of snow melting and data pertaining to other parameters mentioned above [38,39].…”

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confidence: 99%

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confidence: 99%

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

Liu

et al. 2020

Remote Sensing

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Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions. cloud removal techniques, and spatiotemporal data fusion techniques [8][9][10][11]. For example, moderate resolution imaging spectroradiometer (MODIS) data are broadly employed as a reliable data source for detecting the extent of snow cover owing to its high spatial, time, and spectral resolutions [12,13]. Its sensitivity to factors such as aerosol optical properties are also explored in alpine areas [14]. Further, the "subpixel snow-cover information", "empirical relationship assumptions" [15,16], and "spectral unmixing" [17] models have been extensively applied for the inversion of the fractional snow cover. Transient snowline altitude and glacier elevation can be extracted by combining optical and synthetic aperture radar (SAR) imagery as well as DEM data [18]. Passive microwave-based models can penetrate clouds and provide measurements in shadowed regions and hence, are useful for inversion of the snow depth [19,20], snow water equivalent (SWE) [21], and snow cover storage [22]. Based on this, the composite snow cover products ESA GlobSnow SWE dataset [23] and snow data assimilation system (SNODAS) [24] were produced.Microscopically, SAP mainly refers to physical metamorphism as a variation in the grain size and particle structure [1][2][3][4]. The accumulation of pollution in snow, increasing liquid water content, and surface roughness are also typical symptoms of SAP (these also influence snow metamorphism) [5,6]. Extensive research has been conducted on retrieving the snow grain size using "scaled band area" algorithm and the MODIS snow-covered area grain size (MODSCAG) model [15]. On the basis of the quasi-crystalline approximation (QCA) th...

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Snow, Glacier, and Glacier Lake Mapping and Monitoring Using Remote Sensing Data

Thakur

Garg

Nikam

et al. 2021

Water, Cryosphere, and Climate Change in the Himalayas

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Estimation of Snow Cover Area, Snow Physical Properties and Glacier Classification in Parts of Western Himalayas Using C-Band SAR Data

Cited by 40 publications

References 43 publications

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

Snow Depth and Snow Water Equivalent Estimation in the Northwestern Himalayan Watershed using Spaceborne Polarimetric SAR Interferometry

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

Snow, Glacier, and Glacier Lake Mapping and Monitoring Using Remote Sensing Data

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