Abstract: Abstract: Sugarcane identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Information on the sugarcane cultivation areas is of global economic and environmental significance. The study area is Guigang City located in the central area of Guangxi Province which is a good representation of the agricultural conditions. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity. The growing region of sugarcane in Guangxi Province is discrete, so the remote sensing spectral of sugarcane is vulnerable to be impacted on a variety of crops. There are certain difficulties in the use of multi-spectral remote sensing to extract sugarcane. Current techniques for mapping sugarcane are based mainly on MODIS satellite data and may not make full use of the texture characteristics. The objective of this research is to investigate the potential for the application of the China Environment Satellite HJ-1A/1B and Phenology in monitoring sugarcane cultivation areas in Guangxi province in southern China. In our approach, we explored several characteristics such as the time information, spectral characteristics and texture features, used an object-based image analysis method and decision tree method for mapping the sugarcane area over large areas based on multi-temporal China Environment Satellite HJ-1A/B Data. A CCD camera sensor with 30m spatial resolution on board the China Environment Satellite HJ-1A and B has both visible and near infrared bands and a revisit period of four days, thus the temporal Normalized Difference Vegetation Index (NDVI) can be obtained from HJ-1A and B data. A time series of the China Environment Satellite HJ-1A/B Data and DEM images was acquired in order to represent the wide range of pattern variation along the sugarcane crop cycle. Firstly, the phenology differences and between sugarcane and other crops, such as cassavas, rice, corn, in Guigang City were analyzed. Therefore the best recognition sessions of the sugarcane was proposed to be February, May, early August and December. Then, to derive the image objects, the multi-scale segmentation algorithm was used in Definiens Developer in which the classification rule set was established. The rule set consists of the Process Algorithm and Class Description which contain several membership functions of characteristic indexes. The useful characteristic indexes mapping the sugarcane from other crops are Spectral mean value, NDVI, Digital Evaluate Model (DEM) and Gray Level Co-occurrence Matrix (GLCM) homogeneity. Finally, the desired thematic map with sugarcane ready to harvest was generated. The mapping was then evaluated applying the confusion matrix method and Kappa statistics to the independent testing dataset which was composed of 102 field survey points and 48 point samples on Google Earth. The statistics indicated that the classification achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.83. The results of this study show that the Object-Oriented method is very efficient for the sugarcane classification process to maximize elimination of other interfering factors and suggests that the China Environment Satellite HJ-1A/B has great potential in the development of an operational system for monitoring sugarcane growth in southern China.