摘要：Alaska’s Arctic and boreal regions, largely dominated by tundra and boreal forest, are witnessing unprecedented changes in response to climate warming.However, the intensity of feedbacks between the hydrosphere and vegetation changes are not yet well quantified in Arctic regions.This lends considerable uncertainty to the prediction of how much, how fast, and where Arctic and boreal hydrology and ecology will change.With a very sparse network of observations (meteorological, flux towers, etc.) in the Alaskan Arctic and boreal regions, remote sensing is the only technology capable of providing the necessary quantitative measurements of land–atmosphere exchanges of water and energy at regional scales in an economically feasible way.Over the last decades, the University of Alaska Fairbanks (UAF) has become the research hub for high-latitude research.UAF’s newly-established Hyperspectral Imaging Laboratory (HyLab) currently provides multiplatform data acquisition, processing, and analysis capabilities spanning microscale laboratory measurements to macroscale analysis of satellite imagery.The specific emphasis is on acquiring and processing satellite and airborne thermal imagery, one of the most important sources of input data in models for the derivation of surface energy fluxes.In this work, we present a synergistic modeling framework that combines multiplatform remote sensing data and calibration/validation (CAL/VAL) activities for the retrieval of land surface temperature (LST).The LST Arctic Dataset will contribute to ecological modeling efforts to help unravel seasonal and spatio-temporal variability in land surface processes and vegetation biophysical properties in Alaska’s Arctic and boreal regions.This dataset will be expanded to other Alaskan Arctic regions, and is expected to have more than 500 images spanning from 1984 to 2012.
关键词：land surface temperature; Landsat; surface energy balance; Arctic; hyperspectral; HyLab; land surface processes land surface temperature ; Landsat ; surface energy balance ; Arctic ; hyperspectral ; HyLab ; land surface processes
