Crop classification of modern agricultural park based on time-series Sentinel-2 images

Quickly and accurately grasping the spatial distribution of crops, estimating the area and scope of different crops were of great significance for the country to formulate macroscopic agricultural policies and guide farmers in agricultural production. To explore an efficient and accurate crop classification method, this paper took the agricultural area of Jalaid Banner of Hinggan League in Inner Mongolia Autonomous Region of China as the study area and extracted main crop classification based on the Sentinel-2 satellite remote sensing image data from May to October 2019. By analyzing the time-series curves of the four characteristic indexes of NDVI, RVI, EVI and Ref (NIR) in the study area, a total of four classification methods including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Maximum Likelihood (ML) were used to classify various crops in the study area. The RF results were compared with the classification results of DT, SVM and ML, and the spatial distribution of major crops such as rice, corn, stevia, dry rice and soybean were successfully extracted and identified. The results showed that RF had the highest overall classification accuracy of 95.8% with a Kappa coefficient of 0.944, DT, SVM and ML had classification accuracy of 92.2%, 91.6% and 86.5%, respectively. The above results indicate that the multitemporal Sentinel-2 remote sensing images can be extracted by spectral index time-varying features, and the crop classification using the random forest algorithm can obtain high accuracy results, which provides effective technical support for the fine guidance of large-scale agricultural production in the park.