Abstract: Currently, the basic method of monitor winter wheat freeze injury is concentrated on temperature retrieval and compared with normalized difference vegetation index before and after freeze injury. However, temperature is not only reason for freeze injury, and normalized difference vegetation index can lead to overestimate the winter wheat coverage before closing. So this paper selected the analysis of change vector which based on multi-temporal vegetation indexes to improve the monitoring freeze injury accuracy. Winter wheat freeze injury of Gaocheng as study object, various vegetation indexes were extracted from multi-temporal HJ data, change vector was built and the trend of dynamic changing was analyzed, combined with the sensitivity analysis of winter wheat freeze injury spectral character, the model of monitoring freeze injury situation disaster remote sensing was built, and monitoring the degree of growth recover. The result showed that the change vector analysis could reflect the distribution and degree of winter wheat freeze injury and recovery. Meantime the change vector model which based on the structure insensitive pigment index had the highest accuracy during based on the other vegetation indexes model, in addition, the model verification results were 83.3%, 88.9%, respectively. So the method of change vector analysis was effective for monitoring winter wheat freeze injury and growth recovery. This study could supply a new way to monitoring the other crop disaster.