Crop distribution extraction based on Sentinel data

Remote sensing identification and classification of crops is the use of remote sensing for estimating crop planting area of timely and accurate monitoring of crop growth and plant diseases and insect pests in advance to make the product output to estimate the key and premise of the study using Sentinel-1 and Sentinel-2 satellite, by random forest algorithm, the traditional optical wavelengths and vegetation index The backward scattering field of red edge information and radar information in feature selection and feature classification, including winter wheat summer corn orchard woodland town water and bare land set three controls, such as the first group contains radar time characteristics, the characteristics of the second control group contains red edge long, the third group includes traditional vegetation index for phase characteristics, analyzed the different classification accuracy. The results from the confusion matrix show that the red edge band edge after index and the radar scattering information to join the crop classification accuracy is improved effectively. Sentinel optical and radar satellites with a time resolution of 5–6 days have great potential for crop monitoring research