Extraction of winter wheat planting area based on fused active and passive remote sensing images

To obtain fast and accurate crop distribution information, the feasibility of using active remote sensing imagery (Sentinel-1A) and passive remote sensing image(Sentinel-2) to extract the winter wheat spatial distribution was analyzed. Firstly, Sentinel-1A winter wheat image at the whole growth stage were synthesized according to the phenology of winter wheat, and high quality Sentinel-2 images of winter wheat after overwintering were synthesized according to the normalized vegetation index(NDVI) time series curves of various types of features. Three classification schemes, Sentinel-1A images, Sentinl-2 images and fused Sentinel-1A and Sentinl-2 active-passive remote sensing images, were designed, and then winter wheat was classified according to the random forest algorithm on the Google Earth Engine (GEE) cloud platform. The results showed that, accuracy of the user and winter wheat producer accuracy based on Sentinel-1A images at the whole growth stage were 83.15% and 86.44% respectively, and there was more "pepper" noise in the extraction results; the user accuracy and winter wheat producer accuracy based onSentinl-2 images after overwintering was 87.98% and 84.75% respectively, and improved extraction accuracy compared with that of Sentinel1A image at the whole growth stage, but the classification results were influenced by the "same spectrum of foreign matter", resulting in many misclassifications; the user accuracy and winter wheat producer accuracy with fused active and passive remote sensing images were 96.57% and 95.48%, respectively, compared with that of using only a single source of data, the accuracy of classification of winter wheat was improved to different degrees.