Early season crop classification using multi-frequency polarimetric synthetic aperture radar with machine learning methods

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Timely and accurate crop classification is essential for Canada's crop management and food security. Currently, Canadian crop classification maps are only available at the end of each growing season, therefore early season crop classification will help Canada better manage agricultural activities. Most crops have similar spectral signatures and frequent cloudy days in early growing seasons are challenges for optical satellite images. The application of current synthetic aperture radar (SAR) images is limited by longer repeating cycles so the utilization of multi-frequency and multi-angle SAR images are essential for achieving accurate early season crop classification. The objective of this project is to extend the crop growth stage estimation technology of A.U.G. Signals Ltd. (AUG) to achieve early season crop classification using Sentinel-1, TerraSAR-X and simulated RADARSAT constellation mission (RCM) polarimetric SAR (PolSAR) images with state-of-the-art machine learning algorithms. The outcome of the project will help AUG commercialize crop monitoring technologies to provide timely and accurate information for the Canadian agriculture industry.