Information on rice growing areas is important for crop production estimation. This study investigated the applicability of the linear mixture model (LMM) and support vector machines (SVM) to classify rice cropping systems in the Vietnamese Mekong Delta using MODIS data. Data were processed for 2006 comprising three main steps: (1) data pre-processing to generate smooth time-series NDVI data, (2) rice crop classification using LMM and SVM, and (3) classification accuracy assessment using the ground reference data and rice area statistics. The classification results indicated that SVM yielded slightly more accurate results than LMM. The overall accuracy and Kappa coefficient achieved by LMM were 79.9% and 0.73, while those by SVM were 85.1% and 0.80, respectively. The Z-test based on Kappa statistics reported the value of 0.275 smaller than 1.96, indicating no significant difference between the two methods. The comparison results between the MODIS-derived rice area and rice area statistics also affirmed close agreement (R2>0.8), in both cases.