摘要：Obtaining high resolution records of surface temperature from satellite sensors is important in the Arctic because meteorological stations are scarce and widely scattered in those vast and remote regions. Surface temperature is the primary climatic factor that governs the existence, spatial distribution and thermal regime of permafrost which is a major component of the terrestrial cryosphere. Land Surface (skin) Temperatures (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Terra and Aqua satellite platforms provide spatial estimates of near-surface temperature values. In this study, LST values from MODIS are compared to ground-based near-surface air ( T air ) and ground surface temperature (GST) measurements obtained from 2000 to 2008 at herbaceous and shrub tundra sites located in the continuous permafrost zone of Northern Québec, Nunavik, Canada, and of the North Slope of Alaska, USA. LSTs (temperatures at the surface materials-atmosphere interface) are found to be better correlated with T air (1–3 m above the ground) than with available GST (3–5 cm below the ground surface). As T air is most often used by the permafrost community, this study focused on this parameter. LSTs are in stronger agreement with T air during the snow cover season than in the snow free season. Combining Aqua and Terra LST-Day and LST-Nigh acquisitions into a mean daily value provides a large number of LST observations and a better overall agreement with T air . Comparison between mean daily LSTs and mean daily T air , for all sites and all seasons pooled together yields a very high correlation ( R = 0.97; mean difference (MD) = 1.8 °C; and standard deviation of MD (SD) = 4.0 °C). The large SD can be explained by the influence of surface heterogeneity within the MODIS 1 km 2 grid cells, the presence of undetected clouds and the inherent difference between LST and T air . Retrieved over several years, MODIS LSTs offer a great potential for monitoring surface temperature changes in high-latitude tundra regions and are a promising source of input data for integration into spatially-distributed permafrost models.
