AUTOMATIC CORN AND SOYBEAN MAPPING BASED ON DEEP LEARNING METHODS (CASE STUDY: HAMILTON, HARDIN, BOONE, STORY, DALLAS, POLK, AND JUSPER COUNTIES IN LOWA STATE)
Keywords: Corn, Soybean, Landsat-8, NDVI, multi-temporal, Deep Learning
Abstract. Corn and Soybean are important crops for the world’s people. Agricultural planning relies heavily on monitoring and mapping corn and soybean fields. With the development of remote sensing technology and deep learning algorithms, corn and soybean fields are being managed more intelligently nowadays. By using Landsat-8 images with multi-temporal maps of NDVI index, we intend to compare deep learning models such as 1-D CNN, 1-D CNN-LSTM, and 2-D U-net for separating corn and soybean fields from other crops (because soybean and corn fields have similar NDVI curves) in the United States in 2020. It was found that the 2-D U-net model performed the most accurately for corn and soybean classes with Kappa coefficients of 88.48 and 88.89, respectively. This can be explained by the identification of complex features using NDVI multi-temporal indexes of March to November in the United States.