Comparing Remote Sensing Derived Land Surface Temperature and Air Temperature from in-Situ Station on Pan-Arctic Scale
Urban, Marcel1; Eberle, Jonas1; Hüttich, Christian1; Schmullius, Christiane1; Herold, Martin2
1Friedrich-Schiller-University Jena, GERMANY; 2Wageningen University, NETHERLANDS
Land Surface Temperature (LST) is one of the Essential Climate Variables. Satellite-based temperature measurements are an important indicator for global climate change studies over large areas. In this time, records from MODIS, AVHRR and AATSR are providing long-term time series information. So far, Land Surface Temperature evaluation approaches have been carried out for specific areas of interest.
Climate model runs predict an increase in temperature conditions for the arctic regions during the upcoming century, which will have dramatic impacts to the arctic ecosystem. Since the arctic region is highly vulnerable to global climate change, it is required to utilize data, which covers these large regions. Remote Sensing provides a useful tool to extract various land surface parameters over large areas, such as Land Surface Temperature. To identify and quantify temperature changes and trends in the arctic climate system, accurate and consistent satellite measurements are mandatory to meet the requirements of climate research groups. Thus, assessing the quality of remote sensing based temperature measurements giving feedback to the climate modeling community and other users by identifying agreements and discrepancies when comparing to temperature records from meteorological stations.
This paper presents a comparison of state of the art remote sensing based Land Surface Temperature data with Air Temperature measurements from meteorological stations on pan-arctic scale (north of 60° Latitude). Within this study we compared Land Surface Temperature products from AVHRR, MODIS and AATSR with an in-situ Air Temperature database provided by the National Climate Data Center (NCDC).
Despite analyzing the whole acquisition time period of each Land Surface Temperature product, inter annual variability of statistic parameters, which were derived comparing LST and Air Temperature for the overlapping time period of the remote sensing data (2000 - 2005), will be shown. In addition, land cover information was included in the evaluation approach by using GLC2000.