In-situ validation of remotely sensed land surface temperatures in high-arctic land regions – implications for gap filling and trend analyses

Abstract:

We present a summary of validation efforts of MODIS land surface temperature (MOD11A1, MYD11A1) using in-situ observations from the high-arctic sites Ny-Ålesund (79 °N) and Austfonna ice cap (80 °N) on Svalbard, as well as Samoylov Island in NE Siberia (72 °N). For all three sites, multi-year time series of outgoing and incoming long-wave radiation are available from which the skin temperature can be calculated. Our analysis is focused on long-term averages of all-sky temperatures which are required to determine trends of surface temperatures.

At all sites, yearly averages computed from all available MODIS LST measurements are cold-biased by up to 3 °C, which is mainly caused by a significant cold-bias during the winter period. A closer analysis using in-situ observations of cloudiness reveals two main error sources. First, winter surface temperatures are systematically warmer for cloudy skies, so that the satellite predominantly samples “cold” clear-sky conditions. Secondly, the cloud detection algorithm fails to exclude a significant number of cloudy scenes, so that colder cloud top temperatures are contained in the surface temperature record. For the Austfonna ice cap, we estimate that the fraction of such cloud top temperatures could exceed 40%, which highlights the importance of this error source. Over the N Atlantic region, the number of MODIS LST retrievals varies by up to a factor of three, with highest numbers on the Greenland ice sheet and lowest numbers on Iceland the coastal regions of Norway.

When assessing trends in land surface temperatures through remote sensing, three factors must be considered: a) trends in the “true” fraction of cloudy conditions, b) trends in the surface temperature for cloudy conditions, and c) trends in misidentified cloudy scenes and cloud top temperatures.

We demonstrate that a simple gap-filling procedure using downscaled air temperatures from the ERA-interim reanalysis can significantly improve the agreement with in-situ measurements. Such a composite product has the potential to moderate the influence of factors a and b, but cloud top temperatures due to misidentified cloudy scenes are still contained.