Timely and accurate monitoring of forest disturbance is essential to help us understand how the Earth system is changing. MODIS (Moderate Resolution Imaging Spectroradiometer) imagery and subsequent MODIS products provide near-daily global coverage and have transformed the ways we study and monitor the Earth. To monitor forest disturbance, it is necessary to be able to compare observations of the same place from different times, but this is a challenging task using MODIS data as observations from different days have varying view angles and pixel sizes, and cover slightly different areas. In this paper, we propose a method to fuse MODIS and Landsat data in a way that allows for near real-time monitoring of forest disturbance. The method is based on using Landsat time-series images to predict the next MODIS image, which forms a stable basis for comparison with new MODIS acquisitions. The predicted MODIS images represent what the surface should look like assuming no disturbance, and the difference in the spectral signatures between predicted and observed MODIS images becomes the “signal” used for detecting forest disturbance. The method was able to detect subpixel forest disturbance with a producer's accuracy of 81% and a user's accuracy of 90%. Patches of forest disturbance as small as 5 to 7ha in size were detected on a daily basis. The encouraging results indicate that the presented fusion method holds promise for improving monitoring of forest disturbance in near real-time.