Remote Sensing of the Cryosphere

Tedesco, M.

doi:10.1002/9781118368909

Cited by 64 publications

(12 citation statements)

References 8 publications

(12 reference statements)

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“…Sea ice, permafrost, snow, and ice masses (continental ice sheets and mountain glaciers) are key elements of the cryosphere domain. As a result, the scientific study of these components helps to analyse the existing issues of global sea-level, climate and other associated ecological changes (Tedesco, 2015).…”

Section: Remote Sensing For Cryosphere Applicationsmentioning

confidence: 99%

“…Apart from hydrological aspects, detailed snow cover maps are also utilised in weather forecasting and military operations (Miller, Lee, & Fennimore, 2005). Thus, understanding the spatial extent of snow has a variety of applications in the cryosphere paradigm (Allen, Rastner, Arora, Huggel, & Stoffel, 2015;Birajdar, Venkataraman, & Samant, 2016;Man, Guo, Liu, & Dong, 2014;Mankin et al, 2015;Tedesco, 2015;Tekeli, Sönmez, & Erdi, 2016;Zhan et al, 2017).…”

Section: Remote Sensing For Cryosphere Applicationsmentioning

confidence: 99%

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Snow and Cloud Discrimination Using Convolutional Neural Networks

Varshney

Gupta

Persello

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Snow is an important feature on our planet, and measuring its extent has advantages in climate studies. Snow mapping through satellite remote sensing is often affected by cloud cover. This issue can be resolved by using short wave infrared (SWIR) sensors. In order to obtain an effective cloud mask, our study aims to use SWIR data of a ResourceSat-2 satellite. We employ Convolutional Neural Networks (CNN) to discriminate similar pixels of clouds and snow. The technique is expected to give a high accuracy compared to traditional methods such as thresholding. The cloud mask thus produced can also be used for creating the metadata for Indian Remote Sensing products.</p>

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Section: Remote Sensing For Cryosphere Applicationsmentioning

confidence: 99%

Section: Remote Sensing For Cryosphere Applicationsmentioning

confidence: 99%

Snow and Cloud Discrimination Using Convolutional Neural Networks

Varshney

Gupta

Persello

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Theoretically, SWE is defined as the product of snow depth and snow density and can be conceptualised as the amount of liquid water obtained owing to the instantaneous melting of an entire snowpack (Tedesco, 2015). Obtaining accurate estimation of the SD and SWE is quite challenging depending upon the data availability, variety, and quality, parameterisation method, mathematical model selection, and the hydrometeorological conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the difficulties posed by in-situ or ground based measurements of the SD and SWE in rugged terrains, remote sensing techniques coupled with adequately sampled (both in space and time domains) ground measurements are widely used to improve the quality of these estimated parameters over considerably large areas (Takala et al, 2011). Currently, LiDAR (Light Detection and Ranging) and spaceborne SAR (Synthetic Aperture Radar) are the most popular techniques used in the studies related to snow, ice and the cryosphere in general (Deems et al, 2013;Leinss et al, 2014;Tedesco, 2015). However, LiDAR can only be used to determine the height of the snow and cannot be used for measuring other physical properties such as snow density and snow wetness (Tedesco, 2015;Leinss et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Currently, LiDAR (Light Detection and Ranging) and spaceborne SAR (Synthetic Aperture Radar) are the most popular techniques used in the studies related to snow, ice and the cryosphere in general (Deems et al, 2013;Leinss et al, 2014;Tedesco, 2015). However, LiDAR can only be used to determine the height of the snow and cannot be used for measuring other physical properties such as snow density and snow wetness (Tedesco, 2015;Leinss et al, 2014). In addition, the operating cost of LiDAR is sufficiently high and is also weather dependent (Deems et al, 2013).…”

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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Timing and regional patterns of snowmelt on Antarctic sea ice from passive microwave satellite observations

et al. 2016

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An improved understanding of the temporal variability and the spatial distribution of snowmelt on Antarctic sea ice is crucial to better quantify atmosphere-ice-ocean interactions, in particular sea-ice mass and energy budgets. It is therefore important to understand the mechanisms that drive snowmelt, both at different times of the year and in different regions around Antarctica. In this study, we combine diurnal brightness temperature differences (dT B (37 GHz)) and ratios (T B (19 GHz)/T B (37 GHz)) to detect and classify snowmelt processes. We distinguish temporary snowmelt from continuous snowmelt to characterize dominant melt patterns for different Antarctic sea-ice regions from 1988/1989 to 2014/2015. Our results indicate four characteristic melt types. On average, 38.9 6 6.0% of all detected melt events are diurnal freeze-thaw cycles in the surface snow layer, characteristic of temporary melt (Type A). Less than 2% reveal immediate continuous snowmelt throughout the snowpack, i.e., strong melt over a period of several days (Type B). In 11.7 6 4.0%, Type A and B take place consecutively (Type C), and for 47.8 6 6.8% no surface melt is observed at all (Type D). Continuous snowmelt is primarily observed in the outflow of the Weddell Gyre and in the northern Ross Sea, usually 17 days after the onset of temporary melt. Comparisons with Snow Buoy data suggest that also the onset of continuous snowmelt does not translate into changes in snow depth for a longer period but might rather affect the internal stratigraphy and density structure of the snowpack. Considering the entire data set, the timing of snowmelt processes does not show significant temporal trends.

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Remote Sensing of the Cryosphere

Cited by 64 publications

References 8 publications

Snow and Cloud Discrimination Using Convolutional Neural Networks

Snow and Cloud Discrimination Using Convolutional Neural Networks

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

Timing and regional patterns of snowmelt on Antarctic sea ice from passive microwave satellite observations

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