Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland |
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| Institution: | 1. Rangeland Scientist and Research Leader, US Dept of Agriculture–Agricultural Research Service–Rangeland Resources Research Unit, Cheyenne, WY 82009, USA;2. Post-doctoral Research Associate, US Dept of Agriculture–Agricultural Research Service–Rangeland Resources Research Unit, Cheyenne, WY 82009, USA;3. Physical Science Technician, US Dept of Agriculture–Agricultural Research Service–Rangeland Resources Research Unit, Cheyenne, WY 82009, USA;4. Area Statistician US Dept of Agriculture–Agricultural Research Service–Plains Area, Fort Collins, CO 80526, USA;5. Agronomist Cátedra de Forrajicultura, IFEVA, Facultad de Agronomía, Universidad de Buenos Aires, CONICET, C1417DSE, Buenos Aries, Argentina;6. Agronomist, Instituto Nacional de Tecnología Agropecuaria (INTA), Estación Experimental Concepción del Uruguay, Entre Ríos, Argentina |
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| Abstract: | Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models. |
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| Keywords: | Snow cover Hydrological modelling Spatio-temporal analyses Cloud-filling |
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