Planting areas extraction of sugarcane using multi-feature fusion method based on Sentinel-2A satellite images

In order to accurately identify large-scale sugarcane plantations, pre-processed Sentinel-2A remote sensing imagery data from three different time periods were used in this study. The vegetation index features NDVI, BI2 and S2REP were calculated and the optimal texture feature, homogeneity, was selected using principal component analysis and the grey level co-occurrence matrix. A random forest algorithm was then used to construct identification models with different combinations of features, including single time period and single feature, single time period and multiple features, multiple time periods and single feature, and multiple time periods and multiple features. The experiment was conducted in Nansha District, Guangzhou, and the results showed that the fusion of NDVI, BI2, homogeneity and temporal features provided the best identification results for sugarcane planting areas, with clear object boundaries and effective control of salt and pepper noise in the images. The overall accuracy reached 0.974 and the kappa coefficient reached 0.931. In the result validation, the identification accuracy for Nansha District, Guangzhou was 81.6%. The experimental errors were also analysed and the method was found to be suitable for determining the area under sugarcane cultivation in the Pearl River Delta region, providing valuable reference information for the agricultural sector in monitoring the growth of sugarcane, estimating yields and forecasting prices.