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To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean $R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean $R = 0.776$) and LMGM (mean RMSE = 0.074; mean $R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and $R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications. [ABSTRACT FROM AUTHOR]