Raspberry has the reputation of being "the third generation of gold fruits." Obtaining accurate data on the planting area of raspberries is of great significance for adjusting the crop planting structure and industrial development in Shangzhi， the red raspberry country. Taking Zhoujiayingzi village， Weihe town， Shangzhi city， Heilongjiang province as the study area， a high spatial and temporal resolution of Sentinel-2 data was used to obtain time series data of the study area. Using time-series changes in terms of spectral characteristics and normalized vegetation index， the CART algorithm was used to estimate the raspberry planting area in the study area. A comparison with the results of planting areas obtained based only on multi-temporal remote sensing images was performed to explore any differences due to the participation of NDVI time-series data on the area extraction accuracy， and to compare the object-oriented classification and the support vector machine classification based on optimal time-phase data. The experimental results show that the two methods based on the time series CART algorithm obtain better results than the other two classification algorithms in extracting the planting area of raspberries and that they can obtain the planting area and spatial distribution of crops with a higher accuracy， which meets the needs of crop monitoring. NDVI time series data were then added to the multi-temporal data classification so that the spectral difference between crops could be enlarged， and the classification accuracy improved. Compared with only using Sentinel-2 multi-temporal data， the classification accuracy is improved by 1.67% and the Kappa coefficient is improved by 0.02.