Rapid and accurate assessment of global forest cover change is needed to focusconservation efforts and to better understand how deforestation is contributing to thebuildup of atmospheric CO2. Here we examined different ways to use land surfacetemperature (LST) to detect changes in tropical forest cover. In our analysis we usedmonthly 0.05° × 0.05° Terra Moderate Resolution Imaging Spectroradiometer (MODIS)observations of LST and Program for the Estimation of Deforestation in the BrazilianAmazon (PRODES) estimates of forest cover change. We also compared MODIS LSTobservations with an independent estimate of forest cover loss derived from MODISand Landsat observations. Our study domain of approximately 10° × 10° included theBrazilian state of Mato Grosso. For optimal use of LST data to detect changes in tropicalforest cover in our study area, we found that using data sampled during the end of the dryseason (∼1–2 months after minimum monthly precipitation) had the greatest predictiveskill. During this part of the year, precipitation was low, surface humidity was at aminimum, and the difference between day and night LST was the largest. We used thisinformation to develop a simple temporal sampling algorithm appropriate for use inpantropical deforestation classifiers. Combined with the normalized difference vegetationindex, a logistic regression model using day‐night LST did moderately well at predictingforest cover change. Annual changes in day‐night LST decreased during 2006–2009relative to 2001–2005 in many regions within the Amazon, providing independentconfirmation of lower deforestation levels during the latter part of this decade as reported by PRODES.