In order to solve the problems of low precision of cultivated land information extraction due to complex vegetation types， complex terrain and broken plots in southern China， a method of arable land area extraction under complex conditions of object-oriented and cart decision tree is proposed. Taking Longan County and Wuming County of Nanning City， Guangxi as the study area， using Sentinel-2A image， combining digital elevation data DEM and normalized vegetation index NDVI and other multi-source data， using object-oriented segmentation technology to identify plot information， and then using CART decision tree classification method， according to the shape and spectral characteristics of different land types， the cultivated land in the study area is extracted. The results show that the overall precision and Kappa coefficient of the object-oriented CART decision tree classification method are 96.1% and 0.94， respectively. Compared with the total accuracy of cultivated land information extraction of cart decision tree without object-oriented segmentation， the kappa coefficient is increased by 0.54. The object-oriented segmentation method is beneficial to reducing the influence of complex background on the extraction of cultivated land. Based on the object-oriented CART decision tree classification method， the extraction of the cultivated land information in the research area is better than the traditional method， and the extraction precision of the cultivated land information can be improved.

Keywords： Sentinel-2A ; Object-oriented ; CART decision tree classification ; Farmland extraction