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The brightness temperature difference (BTD) between two thermal infrared bands is a common index for dust detection. However, the BTD is sensitive to the observed temperature, which hinders its use in automatic dust detection, especially over desert land surfaces. In this paper, a dynamic reference brightness temperature differences (DRBTD) algorithm was developed to detect dust by removing the influence of the observed temperature on the BTD. Using long-term MODIS observations, the algorithm establishes the clear-sky linear relationships pixel by pixel between the brightness temperatures (BTs) at 12 and 11 mu m channels and the relationships between the BTs at 8.6 and 11 mu m channels. From these relationships, the reference BTDs are dynamically generated according to the observed brightness temperatures. Next, the DRBTDI, which is the difference of the observed BTD and the reference BTD, is created and used to separate the dust from other observed objects. This algorithm is applied to MODIS observations to detect several dust events during the daytime and the nighttime over Mongolia and northwestern and northern China. The results are compared with Ozone Monitoring Instrument aerosol index (OMI AI), MODIS Deep Blue aerosol optical depth (AOD), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations. The comparisons indicate that the DRBTD algorithm can effectively distinguish dust from clouds and land surface. During the daytime, the DRBTDI is correlated with the OMI AI and MODIS AOD with a correlation coefficient of Pearson (r) of 0.79 and 0.77, respectively. At night, the DRBTDI is correlated with the CALIOP dust AOD with an r of 0.78.