Rapid and accurate assessment of global forest cover change is needed to focus conservation efforts and to better understand how deforestation is contributing to the buildup of atmospheric CO<inf>2</inf>. Here we examined different ways to use land surface temperature (LST) to detect changes in tropical forest cover. In our analysis we used monthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations of LST and Program for the Estimation of Deforestation in the Brazilian Amazon (PRODES) estimates of forest cover change. We also compared MODIS LST observations with an independent estimate of forest cover loss derived from MODIS and Landsat observations. Our study domain of approximately 10° × 10° included the Brazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropical forest cover in our study area, we found that using data sampled during the end of the dry season (∼1-2 months after minimum monthly precipitation) had the greatest predictive skill. During this part of the year, precipitation was low, surface humidity was at a minimum, and the difference between day and night LST was the largest. We used this information to develop a simple temporal sampling algorithm appropriate for use in pantropical deforestation classifiers. Combined with the normalized difference vegetation index, a logistic regression model using day-night LST did moderately well at predicting forest cover change. Annual changes in day-night LST decreased during 2006-2009 relative to 2001-2005 in many regions within the Amazon, providing independent confirmation of lower deforestation levels during the latter part of this decade as reported by PRODES. Copyright 2011 by the American Geophysical Union.