A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Snapir, Boris; Momblanch, Andrea; Jain, Sanjay K.; Waine, Toby; Holman, Ian P.

doi:10.1016/j.jag.2018.09.011

Cited by 48 publications

(41 citation statements)

References 37 publications

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“…The RS based snow cover and its properties have direct use as input to snowmelt runoff models (Jain et al, 2009;Prasad and Roy, 2005;Thakur et al, 2009;Aggarwal et al, 2013;Thakur, 2014;Wulf et al, 2016), and as validation and data assimilation in process based hydrological models (Naha et al, 2016;Agarwal et al, 2016). The optical data is capable of giving good quality snow cover maps, but synthetic aperture radar (SAR) data is able to provide, dry/wet snow and snow physical properties as well, due to penetration capability and sensitivity to snow wetness Venkataraman 2007, 2009;Thakur et al, 2012;Snapir et al, 2019). The only major limitation of SAR sensors is no penetration when snow is wet, less temporal resolution and operations in single wavelength (Hallikainen et al, 2001;Thakur et al, 2012).…”

Section: Snow Covermentioning

confidence: 99%

Snow Cover and Glacier Dynamics Study Using C-and L-Band Sar Datasets in Parts of North West Himalaya

Thakur

Garg

Nikam

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The seasonal snow cover and permanent ice in form of Himalayan glaciers provide fresh water to many perineal rivers of Himalayas. The melt water from seasonal snow and glaciers, especially during of 15 March to 15 June acts as important source of water for drinking, hydropower and irrigation requirements of many areas in North India. This work has highlights the use of C-band Synthetic Aperture Radar (SAR) data from RISAT-1, Sentinel-1A and 1B satellites and ALOS-PALSAR-2 PolInSAR data for snow cover and glacier dynamics study for parts of North West Himalaya. Glacier velocity was derived using InSAR based method using 6 day temporal interval images from Sentinel-1 satellites and 14 day interval for PALSAR-2 satellite. High coherence was obtained for main glacier in both the data sets, which resulted accurate line of site (LOS) glacier velocity estimates for test glaciers. These InSAR data glacier velocity results are obtained after a gap of 21 years. Glacier facies was estimated using multi-temporal SAR image composition based classification. All these maps were verified by extensive ground surveys done at these sites during 2014–2017. The time series data of C-band SAR in VV/VH polarisation was also used to map snow cover in test basins of Bhagirathi and Beas River. The VV/VH data clearly shows difference between dry and wet snow, thus helping in improved snow cover mapping using SAR data. This study will help in refining algorithms to be used for such studies using upcoming NASA-ISRO SAR (NISAR) mission.</p>

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Section: Snow Covermentioning

confidence: 99%

Snow Cover and Glacier Dynamics Study Using C-and L-Band Sar Datasets in Parts of North West Himalaya

Thakur

Garg

Nikam

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Nonetheless, information on snow depth is needed in order to check that the balance of snow accumulation and melt is correctly represented in the model [128]. For this purpose, further research to assess the potential of products such as dry/wet snow maps [169] is required.…”

Section: Calibrationmentioning

confidence: 99%

“…Given that the size, remoteness, low population density, and extreme topographical variation of the Himalayan region prevent the establishment of widespread dense ground-based observations, efforts should be directed to strategically design, implement and maintain cost-efficient high elevation monitoring networks in test catchments that enable the validation and improvement of distributed data for meteorological (i.e., remote sensing products, re-analyses and regional climate models) and snow/ice variables (i.e., remote sensing images to derive glacier movement, debris thickness, etc.). While data quality is improved, proposals for the correction of current meteorological products can be found in the literature [4,29,80] as well as novel methods to infer snow/ice mass balance-related processes [168,169]. Where gauged flows are not available, the option to use remotely-sensed river discharges should be explored [181,182].…”

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confidence: 99%

“…Simplified models which do not constrain melt runoff by simulating snowpack and glacier mass balance should be avoided. Due to their relevance in the snowpack and glacier balance in the region, melt models should (where possible) incorporate modelling advances to represent the effects of seasonal behaviour of parameters such as temperature lapse rate which can be estimated with ground [185] or satellite data [186]; snow ageing which can be analysed with remote sensing data [169]; debris thickness effects on albedo through a melting lag factor [82]; meltwater refreezing through additional coefficients [82,187] and using active daily temperature [155]; avalanches by defining a maximum snow water equivalent and a slope threshold [84,128]; and glacier dynamics by adopting basal sliding as the main movement mechanism [84,85].…”

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confidence: 99%

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Current Practice and Recommendations for Modelling Global Change Impacts on Water Resource in the Himalayas

Momblanch

Holman

Jain

2019

Water

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Global change is expected to have a strong impact in the Himalayan region. The climatic and orographic conditions result in unique modelling challenges and requirements. This paper critically appraises recent hydrological modelling applications in Himalayan river basins, focusing on their utility to analyse the impacts of future climate and socio-economic changes on water resource availability in the region. Results show that the latter are only represented by land use change. Distributed, process-based hydrological models coupled with temperature-index melt models are predominant. The choice of spatial discretisation is critical for model performance due to the strong influence of elevation on meteorological variables and snow/ice accumulation and melt. However, the sparsity and limited reliability of point weather data, and the biases and low resolution of gridded datasets, hinder the representation of the meteorological complexity. These data limitations often limit the selection of models and the quality of the outputs by forcing the exclusion of processes that are significant to the local hydrology. The absence of observations for water stores and fluxes other than river flows prevents multi-variable calibration and increases the risk of equifinality. The uncertainties arising from these limitations are amplified in climate change analyses and, thus, systematic assessment of uncertainty propagation is required. Based on these insights, transferable recommendations are made on directions for future data collection and model applications that may enhance realism within models and advance the ability of global change impact assessments to inform adaptation planning in this globally important region.

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“…The pollutant concentration was estimated by a contamination index based on snow reflectance [26][27][28]. The liquid water content could be obtained by water flux models incorporated within the 1-D snow cover model SNOWPACK [29]. By applying joint inversion using multispectral and hyperspectral data, the snowpack roughness can also be extracted [30].…”

mentioning

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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A method for monthly mapping of wet and dry snow using Sentinel-1 and MODIS: Application to a Himalayan river basin

Cited by 48 publications

References 37 publications

Snow Cover and Glacier Dynamics Study Using C-and L-Band Sar Datasets in Parts of North West Himalaya

Snow Cover and Glacier Dynamics Study Using C-and L-Band Sar Datasets in Parts of North West Himalaya

Current Practice and Recommendations for Modelling Global Change Impacts on Water Resource in the Himalayas

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

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