Rice is one of the most important food crops worldwide, and large-scale rice yield estimation is thus crit-ical for planners to formulate successful strategies to address food security and rice grain export issues.This study performed a comparative analysis of multitemporal Moderate Resolution Imaging Spectro-radiometer (MODIS) enhanced vegetation index (EVI) and normalized difference index (NDVI) data forestimating rice crop yields in the Mekong River Delta (MRD), Vietnam. We processed the data for a 10-year period (2002?2011) following three main steps: (1) create a smooth time series of EVI and NDVI data,(2) formulate crop yield models, and (3) validate the model. The comparison results between EVI/NDVI-based estimated yields and the government?s yield statistics indicated a significant relationship betweenthe two datasets (p-value < 0.001). The estimated results produced from EVI-based models were slightlymore accurate than those from NDVI-based models, with the correlation coefficients (R2) ranging from0.62 to 0.71 for spring?winter and 0.4 to 0.56 for summer?autumn rice crops, respectively. The root meansquare error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed theconsistency between EVI-based estimated yields and the government?s yield statistics. The RMSE valuesfor winter?spring and summer?autumn crops were, respectively, 6.9?8.1% and 5.4?6.7%, and MAE valueswere 5.4?6.7% and 6.5?9.5%. There was, however, a significant correlation between the estimated yieldsobtained from EVI- and NDVI-based models (p-value < 0.001), indicating no significant difference in theestimated yields between these two models. This study demonstrates advantages of using multitemporalMODIS EVI data for large-scale estimation of rice crop yields using the heading date in the MRD prior tothe harvest period, and thus the methods could be transferable to other regions.