Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998–2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.