Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

Wang, Yunlong; Huang, Xiaodong; Liang, Hui; Sun, Yanhua; Feng, Qi; Liang, Tiangang

doi:10.3390/rs10010136

Cited by 55 publications

(45 citation statements)

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“…On a global scale snow cover has a strong interaction with the cryosphere and ocean systems and therefore the climate system of the Earth. The two main effects of snow on the cryosphere are its control on the reflection of radiation, reaching the surface of Earth and balancing its radiation budget (Barry, 2002;Warren, 1982) and isolating properties which can influence the length of the growing season (Keller et al, 2005;Barichivich et al, 2013). Snow albedo dominates the control of its irradiance feedback, which depends on various factors such as snow depth, snow cover extent, vegetation and cloud cover (Fernandes et al, 2009;Qu and Hall, 2007).…”

Section: Introductionmentioning

confidence: 99%

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Gunnarsson

Garðarsson

Sveinsson

2019

Hydrol. Earth Syst. Sci.

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This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November until mid January. In this study MODIS snow cover data were validated over Iceland with comparison to manned in situ observations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R 2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.

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Section: Introductionmentioning

confidence: 99%

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Gunnarsson

Garðarsson

Sveinsson

2019

Hydrol. Earth Syst. Sci.

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“…In recent years, many regions in the Northern Hemisphere have experienced heavy snow storms in the autumn or winter (e.g., the winters of 2009/2010 and 2010/2011). Partly for this reason, linear trends for autumn and winter suggest an increase in snow cover [16,24,[36][37][38], or at least that snow cover has remained reasonably constant [39]. This is in sharp contrast to the climate models which (as explained above) have predicted a continual decrease for all four seasons.…”

Section: Introductionmentioning

confidence: 85%

Northern Hemisphere Snow-Cover Trends (1967–2018): A Comparison between Climate Models and Observations

Connolly¹,

Connolly²,

Soon³

et al. 2019

Geosciences

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Observed changes in Northern Hemisphere snow cover from satellite records were compared to those predicted by all available Coupled Model Intercomparison Project Phase 5 (“CMIP5”) climate models over the duration of the satellite’s records, i.e., 1967–2018. A total of 196 climate model runs were analyzed (taken from 24 climate models). Separate analyses were conducted for the annual averages and for each of the seasons (winter, spring, summer, and autumn/fall). A longer record (1922–2018) for the spring season which combines ground-based measurements with satellite measurements was also compared to the model outputs. The climate models were found to poorly explain the observed trends. While the models suggest snow cover should have steadily decreased for all four seasons, only spring and summer exhibited a long-term decrease, and the pattern of the observed decreases for these seasons was quite different from the modelled predictions. Moreover, the observed trends for autumn and winter suggest a long-term increase, although these trends were not statistically significant. Possible explanations for the poor performance of the climate models are discussed.

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“…Finally, we obtained 620 ski areas, including 589 alpine ski areas, 29 indoor snow centers and 2 non-operational snow fields for backcountry skiing. The digital elevation (DEM) and slope with a resolution of 30 m were originally from ASTER GDEM V2; a classified geomorphologic map of China at a scale of 1:1000,000, and grid datasets of the GDP and population density with a resolution of 1 km were obtained from Resources and Environmental Scientific Data Center (RESDC), Chinese Academy of Sciences (CAS); the monthly datasets of the grid-based surface air temperature with a spatial resolution of 0.5 × 0.5 degrees, and precipitation were obtained from National Meteorological Information Center of China; the daily dataset of the snow depth was retrieved by the revised Chang algorithm [53] with passive microwave brightness temperatures of the SMMR (1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987), SSM/I and SSMI/S (2008-2016), and was obtained from Environmental and Ecological Science Data Center for West China; and the average snow cover duration data during 2000-2016 were from multi-source remote sensing data by Y. Wang et al [54]. Traffic data including highway and railway were obtained from the national fundamental geographic information dataset at a 1:1000,000 scale from National Catalogue Service For Geographic Information.…”

Section: Data Sourcesmentioning

confidence: 99%

The Spatial Pattern of Ski Areas and Its Driving Factors in China: A Strategy for Healthy Development of the Ski Industry

Xiao

Ding

2019

Sustainability

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The development of ski areas would bring socio-economic benefits to mountain regions. At present, the ski industry in China is developing rapidly, and the number of ski areas is increasing dramatically. However, the understanding of the spatial pattern and driving factors for these ski areas is limited. This study collected detailed data about ski areas and their surrounding natural and economic factors in China. Criteria for classification of ski areas were proposed, and a total of 589 alpine ski areas in China were classified into three types: ski resorts for vacationing (va-ski resorts), ski areas for learning (le-ski areas) and ski parks to experience skiing (ex-ski parks), with proportions of 2.1%, 15.4% and 82.5%, respectively, which indicated that the Chinese ski industry was still dominated by small-sized ski areas. The overall spatial patterns of ski areas were clustered with a nearest neighbor indicator (NNI) of 0.424, in which ex-ski parks and le-ski areas exhibited clustered distributions with NNIs of 0.44 and 0.51, respectively, and va-ski resorts were randomly distributed with an NNI of 1.04. The theory and method of spatial autocorrelation were first used to analyze the spatial pattern and driving factors of ski areas. The results showed that ski areas in cities had a positive spatial autocorrelation with a Moran’s index value of 0.25. The results of Local Indications of Spatial Association (LISA) showed that ski areas were mainly concentrated in 3 regions: the Beijing-centered Yanshan-Taihang Mountains and Shandong Hill areas, the Harbin-centered Changbai Mountain areas and the Urumqi-centered Tianshan-Altay Mountain areas. The first location was mainly driven by socio-economic factors, and the latter two locations were mainly driven by natural factors. Ski tourism in China still faces many challenges. The government sector should strengthen supervision, develop a ski industry alliance, and promote the healthy and sustainable development of the ski industry in the future.

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Tracking Snow Variations in the Northern Hemisphere Using Multi-Source Remote Sensing Data (2000–2015)

Abstract: Multi-source remote sensing data were used to generate 500-m resolution cloud-free daily snow cover images for

Cited by 55 publications

References 55 publications

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Northern Hemisphere Snow-Cover Trends (1967–2018): A Comparison between Climate Models and Observations

The Spatial Pattern of Ski Areas and Its Driving Factors in China: A Strategy for Healthy Development of the Ski Industry

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