Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing classification. Google Earth Engine (GEE), a cloud computing analysis platform based on global geospatial analysis, provides a new opportunity for rapid analysis of remote sensing data. In this study, high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in China. The random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020, and Sentinel-2 imagery was used to verify the results. The spectral indices, texture, and terrain features of the image were derived, and their contribution to the classification accuracy of winter wheat was evaluated by scoring. Then the top nine features were selected to form an optimal feature subset. Comparing the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier, the results show that the accuracy difference between the two feature classification schemes is small, but the classification effect of all feature sets is slightly better than the optimal feature subset. The overall classification accuracy of sample plots verification was 86%-95%, the Kappa coefficient was between 0.70 and 0.85, and the percentage error of the total area was 5.42%. The research demonstrates a reliable method for mapping a wide range of winter wheat planting area, and provides a good prospect for exploring the precise mapping of other crops, which is of great significance to crop monitoring and agricultural development.