Crop Planting Structure Extraction in Zhangye Irrigation Area Based on Remote Sensing Images

Crop planting structure extraction based on remote sensing images has been widely used in practice,in which the selection of clas‐sification features and samples are the key factors affecting the extraction accuracy.In order to explore the influence of different classification features and samples on the extraction accuracy of crop planting structure,with the study area in Zhangye Irrigation Area in Gansu Province,this paper uses support vector machine supervised classification method of studying the extraction accuracy of crop planting structure of spec‐tral and temporal NDVI classification features under different samples.The results show that：①With the increase in the number of sam‐ples,the accuracy of the spatial distribution of the identified crop planting structure gradually increase to a stable state.②The average error of corn area extracted by temporal NDVI is 2.82%,the average overall classification accuracy is 84.8%,and the average Kappa coefficient is0.81.The accuracy of crop planting structure based on temporal NDVI feature extraction is better than that of spectral feature extraction.③When the number of samples per 10km~2 in the study area is 3～4,the samples can maintain the best training effect.The research results can provide an important reference for improving the extraction accuracy of crop planting structure.