Study On Crop Classification And Area Extraction Based On Multi-source Remote Sensing Data Of Sentinel

It plays an important role in national food policy and economic plan to acquire spatial distribution,planting area and yield information of crops quickly and accurately.Remote sensing technology is one of the most important ways for agricultural situation surveillance.Currently,most studies on crop classification and area extraction are based on optical remote sensing data such as Landsat data,SPOT data and high-resolution satellite data,and radar remote sensing data such as RADARSAT.Study on radar and optical remote sensing data from the newly launched Sentinel systems needs to be explored.This paper taking Sentinel-1 and Sentinel-2 multi-source remote sensing data as data source,chooses the optimal temporal data and the one with some cloud.Then,four commonly adopted classification methods are utilized to crop classification and area extraction.From the experimental results,we can see the advantages with Sentinel multi-source remote sensing data in the cases of both cloud and no cloud in crop classification.Meanwhile,the application with Sentinel multi-source remote sensing data in area extraction has been clearly proved.The main conclusions are drawn as follows:(1)Classification results of 12-band multi-source remote sensing data are better than those of the 6-band.In the case of no cloud cover,the classification accuracy of 12-band multi-source remote sensing data is 93.47%,and the Kappa coefficient is 87.07%.In the case of cloud cover,the classification accuracy of 12-band multi-source remote sensing data classification result is decreased to 90.25%,and the Kappa coefficient is only 81.58%.(2)Classification results of Sentinel multi-source remote sensing data are better than those of optical data.Without cloud cover,the crop classification results of multi-source remote sensing data by the method of BP neural network are improved significantly,and overall accuracy is improved by 3% and Kappa coefficient is raised by 6%;With a small amount of cloud cover,the crop classification results of multi-source remote sensing data by the method of BP neural network are also improved significantly.Overall classification accuracy and Kappa coefficient are increased by nearly 7% and 14%,respectively.(3)Results of winter wheat planting area extraction with Sentinel multi-source remote sensing data are better.The result of winter wheat planting area extraction based on Sentinel multi-source remote sensing data shows that the minimum distance method can get the best identification result.The accuracy of identification reaches 90.22%,which meets the practical application requirements.Finally,the winter wheat planting area is about 11.1131 square kilometers,accounting for 8.23% of the total area of Yangling.