A review of global satellite-derived snow products

Frei, Allan; Tedesco, Marco; Lee, Shihyan; Foster, James L.; Hall, Dorothy K.; Kelly, Richard; Robinson, David A.

doi:10.1016/j.asr.2011.12.021

Cited by 264 publications

(197 citation statements)

References 141 publications

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“…A very common parameter measured by remote-sensing instruments is snow-covered area (SCA). Operational products on a global scale, such as MODIS snow-cover products (Hall et al, 2002) or GlobSnow (Koetz et al, 2008), are widely used today (Frei et al, 2012). For example, Dozier (1989), Nolin and Dozier (1993), Fily et al (1997) and Dozier et al (2009) published investigations into snow grain size with finer spatial resolution on a regional scale.…”

Section: Y Bühler Et Al: Snow Depth Mapping In High-alpine Catchmentsmentioning

confidence: 99%

Snow depth mapping in high-alpine catchments using digital photogrammetry

Bühler¹,

et al. 2015

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Abstract. Information on snow depth and its spatial distribution is crucial for numerous applications in snow and avalanche research as well as in hydrology and ecology. Today, snow depth distributions are usually estimated using point measurements performed by automated weather stations and observers in the field combined with interpolation algorithms. However, these methodologies are not able to capture the high spatial variability of the snow depth distribution present in alpine terrain. Continuous and accurate snow depth mapping has been successfully performed using laser scanning but this method can only cover limited areas and is expensive. We use the airborne ADS80 optoelectronic scanner, acquiring stereo imagery with 0.25 m spatial resolution to derive digital surface models (DSMs) of winter and summer terrains in the neighborhood of Davos, Switzerland. The DSMs are generated using photogrammetric image correlation techniques based on the multispectral nadir and backward-looking sensor data. In order to assess the accuracy of the photogrammetric products, we compare these products with the following independent data sets acquired simultaneously: (a) manually measured snow depth plots; (b) differential Global Navigation Satellite System (dGNSS) points; (c) terrestrial laser scanning (TLS); and (d) ground-penetrating radar (GPR) data sets. We demonstrate that the method presented can be used to map snow depth at 2 m resolution with a vertical depth accuracy of ±30 cm (root mean square error) in the complex topography of the Alps. The snow depth maps presented have an average accuracy that is better than 15 % compared to the average snow depth of 2.2 m over the entire test site.

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Section: Y Bühler Et Al: Snow Depth Mapping In High-alpine Catchmentsmentioning

confidence: 99%

Snow depth mapping in high-alpine catchments using digital photogrammetry

Bühler¹,

et al. 2015

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“…The Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) supplied SWE and brightness temperature (BT) products Frei et al, 2012). Previous research has shown that AMSR-E BT data can simulate snow depth of the Heihe River watershed and Xinjiang region in China with accuracy above 78 % (Che et al, 2004Dai et al, 2012).…”

Section: Snow Remote Sensing Monitoring Datamentioning

confidence: 99%

“…Many studies have been conducted on snow cover area monitoring (Gutzler and Rosen, 1992;Hall et al, 2001;Liang et al, 2008b;Wang and Xie, 2009;Gao et al, 2010;Paudel et al, 2011), snow depth (SD) simulation (Stowe et al, 1991;Chang et al, 1996;Frei et al, 2012;Yu et al, 2012), snow disaster risk assessment (Romanov et al, 2002), loss evaluation post-disaster (i.e., the research for the pastoral areas after snow disasters have occurred and have resulted in losses of livestock) (Nakamura and Shindo, 2001), snow disaster and avalanche mapping, as well as their relations to climate change (Jones and Jamieson, 2001;Hendrikx et al, 2005;Bocchiola et al, 2008;Delparte et al, 2008;Hirashima et al, 2008;Lato et al, 2012). In 2006, the Chinese government issued a national standard for grading snow disasters in pastoral area (GB/T 20482-2006) (General Administration of Quality Supervision of China, 2006).…”

Section: Introductionmentioning

confidence: 99%

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Wang

Liang

Huang

et al. 2013

Nat. Hazards Earth Syst. Sci.

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This study develops a model for early warning of snow-caused livestock disasters on a county basis and proposes a method of qualitative risk assessment of snow disasters at 500 m resolution for pastoral areas on the Tibetan Plateau (TP). Data used for the model development include remote sensing data, statistical data of weather, livestock, and social economy, and 45 typical snow disaster cases from 2000 to 2010. The principal component analysis (PCA) approach is used to choose 7 crucial factors that contribute over 85% of information for early warning snow disasters on the TP. They are mean annual probability of snow disaster, number of snow-covered days, livestock stocking rate, continual days of mean daily temperature below −10 °C, grassland burial index, rate of snow-covered grassland, and per livestock gross domestic product. The chosen 411 cases from 2008 to 2010 are used to validate the prediction results from the developed early warning model, with an overall accuracy of 85.64% in predicting snow disasters and no disasters. This suggests that the early warning approach developed in the study has operational potential for predicting snow disasters on the TP

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“…Details about the challenges to map snow cover and discriminate between clouds and snow have been discussed extensively in literature. The interested reader should refer to [18][19][20][21] for more information.…”

Section: Introductionmentioning

confidence: 99%

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

et al. 2014

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Central Asia consists of the five former Soviet States Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, therefore comprising an area of ~4 Mio km 2 . The continental climate is characterized by hot and dry summer months and cold winter seasons with most precipitation occurring as snowfall. Accordingly, freshwater supply is strongly depending on the amount of accumulated snow as well as the moment of its release after snowmelt. The aim of the presented study is to identify possible changes in snow cover characteristics, consisting of snow cover duration, onset and offset of snow cover season within the last 28 years. Relying on remotely sensed data originating from medium resolution imagers, these snow cover characteristics are extracted on a daily basis. The resolution of 500-1000 m allows for a subsequent analysis of changes on the scale of hydrological sub-catchments. Long-term changes are identified from this unique dataset, revealing an ongoing shift towards earlier snowmelt within the Central Asian Mountains. This shift can be observed in most upstream hydro catchments within Pamir and Tian Shan Mountains and it leads to a potential change of freshwater availability in the downstream regions, exerting additional pressure on the already tensed situation.

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A review of global satellite-derived snow products

Cited by 264 publications

References 141 publications

Snow depth mapping in high-alpine catchments using digital photogrammetry

Snow depth mapping in high-alpine catchments using digital photogrammetry

Early warning of snow-caused disasters in pastoral areas on the Tibetan Plateau

Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data

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