Image blending is one of the alternative methods to fill temporal gaps in the monitoring of historical vegetation properties using continuous NDVI derived from Landsat 5 TM / 7 ETM+ and 8 OLI images. Frequent cloud occurrence in the tropical upstream catchment limits the use of image blending methods that allow to employment of a single pair base reference. This study aims to evaluate two image blending methods with nine input data configurations to select the most applicable one. Scatter plots and statistical indices such as ME, RMSE, model efficiency and structure similarity showed FSDAF outperforms STARFM in generating both synthetic Landsat 8 OLI NDVI and Landsat 5 TM / 7 ETM+ NDVI when employing unsupervised and supervised classification images, respectively, where both were applied along with MODIS NDVI 250 m v.005. When generating synthetic Landsat 5 TM / 7 ETM+ NDVI using AVHRR NDVI, both algorithms performed similarly. However, when considering the temporal over spatial variance ratio between base reference and predictor images, both algorithms performed almost similar when the value close to minimum. This study shows that selection of image blending algorithm with use single pair base reference image should consider input data configuration and temporal over spatial variance ratio. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.