Abstract: The extreme high and low temperatures, drought events increased and strengthened associated with global warming. It has been a threat to crop growth and production. At present, the seedling situation and disaster monitoring rely solely on the single-phase remote sensing data. Different methods are difficult to be compared due to the difficulty of forming the same standard on the crop disaster and seedling growth between different methods. In this study, a disaster and growth monitoring system was established based upon the vegetation index time series data, the historical growth of the seedling and crop disaster. The system takes long time series of the seedling historical growth and the crop disaster as the evaluation criteria, uses the long time series of MODIS secondary data, which is quasi real-time and multispectral, as well as the vegetation index product data, achieves the automatic download of remote sensing data, the pre-processing of MODIS images and the extracts the basic parameters of the crop responsing to drought, snow, and disaster, as well as a set of simplified business process including thematic map production etc. The system was employed in Tibet pasture/crop condition as monitoring platforms, It is shown that the system can be applied to large area crop’s and disaster monitoring, as well as yield prediction.