Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China

Zhao, Shuhe; Jiang, Tenglong; Wang, Zhaojun

doi:10.3390/rs5010238

Cited by 11 publications

(14 citation statements)

References 20 publications

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“…The classification of Hyperion data using the SVM algorithm provides satisfying results for the classes snow and bare ice (user's accuracies of 90 and 70 %), confirming the successful use of Hyperion for snow and ice monitoring (Bindschadler and Choi, 2003;Casey et al, 2012;Doggett et al, 2006;Negi et al, 2013;Zhao et al, 2013). For other land cover classes (e.g., rocks and debris cover), a lower user's accuracy was obtained (60-70 %), probably due to both the coarse spatial resolution (30 m) of Hyperion for mapping these classes and the spectral similarity between the two classes.…”

Section: Discussionmentioning

confidence: 64%

Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps)

et al. 2017

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Abstract. The amount of reflected energy by snow and ice plays a fundamental role in their melting processes. Different non-ice materials (carbonaceous particles, mineral dust (MD), microorganisms, algae, etc.) can decrease the reflectance of snow and ice promoting the melt. The object of this paper is to assess the capability of field and satellite (EO-1 Hyperion) hyperspectral data to characterize the impact of light-absorbing impurities (LAIs) on the surface reflectance of ice and snow of the Vadret da Morteratsch, a large valley glacier in the Swiss Alps. The spatial distribution of both narrow-band and broad-band indices derived from Hyperion was analyzed in relation to ice and snow impurities. In situ and laboratory reflectance spectra were acquired to characterize the optical properties of ice and cryoconite samples. The concentrations of elemental carbon (EC), organic carbon (OC) and levoglucosan were also determined to characterize the impurities found in cryoconite. Multi-wavelength absorbance spectra were measured to compare the optical properties of cryoconite samples and local moraine sediments. In situ reflectance spectra showed that the presence of impurities reduced ice reflectance in visible wavelengths by 80-90 %. Satellite data also showed the outcropping of dust during the melting season in the upper parts of the glacier, revealing that seasonal input of atmospheric dust can decrease the reflectance also in the accumulation zone of the glacier. The presence of EC and OC in cryoconite samples suggests a relevant role of carbonaceous and organic material in the darkening of the ablation zone. This darkening effect is added to that caused by fine debris from lateral moraines, which is assumed to represent a large fraction of cryoconite. Possible input of anthropogenic activity cannot be excluded and further research is needed to assess the role of human activities in the darkening process of glaciers observed in recent years.

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Section: Discussionmentioning

confidence: 64%

Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps)

et al. 2017

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“…Zhao et al () observed the relationship between the snow grain size and reflectance at 1,033 nm and the results indicated fair linear and exponential relationships between snow reflectance and snow grain size. The correlation coefficients (R) of the two models were 0.81 and 0.84, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Snow is an important constituent of the mountain cryosphere and a critical component in high mountain hydrology. As snow could cause natural hazards like snow avalanches, it has remained a subject of the longstanding monitoring program in alpine regions around the globe (Sibandze, Mhangara, Odindi, & Kganyago, 2014;Zhao, Jiang, & Wang, 2013). Snow cover dynamics has a significant effect upon the high-altitude Himalayan climate as well (Immerzeel, Droogers, de Jong, & Bierkens, 2009).…”

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confidence: 99%

“…Snow wetness and density have been estimated using multispectral satellite images with high, moderate, and low spatial resolutions (e.g. AVHRR, MODIS, Landsat), spaceborne and airborne hyperspectral images from Hyperion, AVIRIS and AVIRIS-NG and field-based data collected using instruments like spectrometers (Haq, 2014;Kay, Alan, Hansen, & Pettit, 2003;Lyapustin et al, 2009;Negi & Kokhanovsky, 2011;Nolin & Dozier, 1993;Painter et al, 2009;Scambos, Haran, Fahnestock, Painter, & Bohlander, 2007;Shekhar, Srivastava, Negi, & Dwivedi, 2018;Zhao et al, 2013). Several researchers have studied various snow properties like snow wetness, snow grain size, snow albedo, fractional snow cover, and radiative forcing by contaminants (Nolin & Dozier, 2000;Negi & Kokhanovsky, 2011).…”

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confidence: 99%

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Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

Haq

Ghosh

Rahaman

et al. 2019

Natural Resource Modeling

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This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment. Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions. In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships. Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

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“…Successful applications with imaging spectroscopy data cover all environments, including the analysis of managed forests [5,6], agricultural areas [7][8][9], mining sites [10,11], urban areas [12][13][14], of unmanaged forests [15,16] and (quasi-)natural ecosystems [17][18][19], including deserts [20,21] or snow and ice [22,23], as well as inland waters and oceans [24,25]. Given the broad range of applications, a great variety of analysis approaches is used with imaging spectroscopy data, e.g., mapping and monitoring [26,27], empirical modeling [5,28,29] or physical-based modeling, especially with radiative transfer models [6,30,31].…”

Section: Introductionmentioning

confidence: 99%

The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

et al. 2015

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Abstract:The EnMAP-Box is a toolbox that is developed for the processing and analysis of data acquired by the German spaceborne imaging spectrometer EnMAP (Environmental Mapping and Analysis Program). It is developed with two aims in mind in order to guarantee full usage of future EnMAP data, i.e., (1) extending the EnMAP user community and (2) providing access to recent approaches for imaging spectroscopy data processing. The software is freely available and offers a range of tools and applications for the processing of spectral imagery, including classical processing tools for imaging spectroscopy data as well as powerful machine learning approaches or interfaces for the integration of methods available in scripting languages. A special developer version includes the full open source code, an application programming interface and an application wizard for easy integration and documentation of new developments. This paper gives an overview of the EnMAP-Box for users and developers, explains typical workflows along an application example and exemplifies the concept for making it a frequently used and constantly extended platform for imaging spectroscopy applications. OPEN ACCESSRemote Sens. 2015, 7 11250

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Snow Grain-Size Estimation Using Hyperion Imagery in a Typical Area of the Heihe River Basin, China

Cited by 11 publications

References 20 publications

Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps)

Impact of impurities and cryoconite on the optical properties of the Morteratsch Glacier (Swiss Alps)

Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

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