Abstract: Supported by the object-oriented technology, information on sugarcane and rice fields were acquired from the high temporal-and-spatial resolution ZY-3 remote sensing images. Using the crop plots as the objects, the multi-temporal spectral signatures from HJ-1 and GF-1 sensors were integrated, and the time series spectrum signatures at critical growth points were captured. Based on them, the cropping models showing phenology differences on selected features of sugarcane and rice fields were constructed. The overall classification accuracy of the models was 86.80%, the Kappa coefficient approximately 0.84, and the overall classification effect desirable. The mapping accuracy and user accuracy for sugarcane reached 92.11% and 90.91%, respectively; and those for rice, 88.89% and 90.91%, respectively. It appeared that the national high temporal-and-spatial resolution satellite imaging system could provide adequate information for proper management on the planting area and structure for the crops.