Climate change is affecting aspects of life across the globe. Nowhere is this more prevalent than in the High Northern Latitudes where the Arctic Amplification of climate change has resulted in rates of warming that are twice the global average. Rising air temperatures drive deeper thawing of permafrost which is expressed, among many other ways, through changes in surface water extent. In this dissertation I developed annual maps of surface water extent from a 30 year series of satellite observations from Landsat over a large region of North American tundra. These maps were used in an object based approach to identify water bodies that show a significant trend in surface area over the past 30 years. Over 25% of the 675,000 water bodies in my study region experienced a statistically significant (p<0.05) trend in surface area change between 1985 and 2015. The analysis reveals that water bodies with a net increasing trend and those with a net decreasing trend are spatially clustered. A distinct pattern of increasing extent in the Northwest and decreasing extent in the Southeast of the study region became clear when change was related to specific watersheds. The watersheds that were dominated by decreasing extent were found to be correlated with presence of bedrock on the surface indicating that shallow soils limit subsurface connectivity and enhance potential for evaporation. There is limited observational data for climate and weather in the region with only four weather stations unevenly distributed in the study region. Therefore, reanalysis data from Modern Era retrospective Reanalysis for Research and Applications (MERRA-2) is used to explore potential climate drivers of surface water change. Surface temperature and ground heating flux in the spring and fall transition periods (shoulder seasons) were found to be good predictors of surface water change. The methodological advances in this dissertation including the object based analysis of water bodies through annual time series and the use of machine learning techniques in high end computing will facilitate future continental scale assessments of surface water extent and the attribution of that change to environmental drivers of change.