Goossens, Rudi

Van De Kerchove, Ruben

English

Ghent University Academic Bibliography

Geophysical Research AbstractsVol. 12, EGU2010-830, 2010EGU General Assembly 2010© Author(s) 2009Ground surface temperatures (GST) modeling in the Russian AltayMountains by using MODIS Land Surface Temperatures (LST).Assessment of the impact of snow cover, topography, landcover andsub-pixel variability on the GST-LST relationship.Ruben Van De Kerchove and Rudi GoossensDepartment of Geography, Ghent University, Ghent, Belgium (ruben.vandekerchove@ugent.be)The Russian Altay Mountains are a challenging area for large scale permafrost modeling. The lack of meteo-data,strong temperature-inversions, rapid changing snow cover patterns and complicated landcover demand an in-depthapproach. As a solution, time and spatially covering MODIS land surface temperature (LST) might be used as aproxy replacing the interpolated air and ground surface temperatures (GST).Recent studies show the potential of this method on large continental areas (e.g. Canadian Arctic, Siberia), byusing sinusoidal fits to eliminate the data-gaps both spatially as temporally out of the time-series. These studiesuse isotherms and analytical solutions for freezing and thawing to model permafrost distribution. However to usethe LST-values as an upper boundary condition at heterogeneous mountain ranges as the Russian Altay, furtherresearch needs to be conducted. In detail the relation between this parameter and surface temperatures beneathareas covered with snow and vegetation requires more attention. In addition the effect of sub-pixel variability andtopographic influence needs to be considered as the LST pixels come at 1km resolution.This study tries to answer these questions by showing results of 96 surface temperature time-series recorded inand around the valley of Dzhazator and on the Tarkhata plain (Kosh Agatch District) from July 2008 until July2009, areas both characterized by discontinuous permafrost, together with spatial dynamics in LST, GST, snowcover and NDVI.iButtons and Onset dataloggers were installed in order to cover surface temperatures beneath a broad range oflandcovers, different topographical positions and in grids to measure the sub-grid variability. LST time-serieswere interpolated by using the relationship with air temperatures. This enables to incorporate high frequencytemperature variations in GST modeling.Correlations both for the summer as the winter season are presented between LST, GST, snow cover and NDVI(Normal Difference Vegetation Index) time-series. Sub-grid variability is examined by a combination of SPOTlandcover classification (20m resolution), ASTER DEM (30m resolution) and multiple temperature observationsin one MODIS pixel.Results show the potential of this method to model surface temperatures, and in a next step, permafrost, in areaswithout any other temperature observations, solely on satellite observations.

Ground surface temperatures (GST) modeling in the Russian Altay Mountains by using MODIS Land Surface Temperatures (LST): assessment of the impact of snow cover, topography, landcover and sub-pixel variability on the GST-LST relationship

https://biblio.ugent.be/publication/1046273/file/1046297.pdf

Ground surface temperatures (GST) modeling in the Russian Altay Mountains by using MODIS Land Surface Temperatures (LST): assessment of the impact of snow cover, topography, landcover and sub-pixel variability on the GST-LST relationship

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