The ESA DUE Permafrost project (2009-2011) is developing a suite of parameters indicative of the subsurface phenomenon permafrost using satellite remote sensing: Land Surface Temperature (LST), Surface Soil Moisture (SSM), Surface Frozen and Thawed State (Freeze/Thaw), Terrain, Land Cover (LC), and Surface Water (SW). Snow parameters (Snow Extent and Snow Water Equivalent) are being developed through the DUE GlobSnow project, Global Snow Monitoring for Climate Research (2008-2011). The final DUE Permafrost remote sensing products cover the years 2007 to 2011 with a circumpolar coverage that will soon be released (early 2012), and then be used to analyze the temporal dynamics and map the spatial patterns of indicators. Further information is available at www.ipf.tuwien.ac.at/ permafrost. Since the beginning, scientific stakeholders and the International Permafrost Association (IPA) have been involved in the science and implementation plan. Interactive international user workshops took place in 2010 at the Technical University of Vienna, Vienna (AT), and in 2011 at the International Arctic Research Center (IARC), Fairbanks, Alaska (US). This involvement and the ongoing evaluation of the indicators derived from remote sensing for the high-latitude permafrost regions make the DUE Permafrost products trustworthy for the permafrost and the climate research community. The adaption of the remote sensing products for the permafrost and climate modelling is experimental and highly dependent on the users’ involvement. For a few years already, the Geophysical Institute Permafrost Laboratory (GIPL), University of Alaska Fairbanks, US, (http://www.gi.alaska.edu/research/snowicepermafrost/Permafrost) has successfully demonstrated the value of using LST derived from remote sensing data for driving its permafrost models. Further experimental testing of the DUE Permafrost products for use by the modeling community (permafrost and climate) will range from (i) the evaluation of external data of the models, with modifying or providing new external data (e.g. tundra land cover, surface water ratio, soil distribution), to (ii) new drivers for regional models derived from remote sensing (e.g., LST), to (iii) the evaluation of the output data from the models (e.g. spatial patterns of moisture and temperature).