Monitoring Crop Condition Using Polarimetric SAR

A changing climate is bringing uncertainty to agricultural production and methods that can deliver assessments of crop condition will help mitigate impacts. Agriculture and Agri-Food Canada is developing a vegetation index based on Synthetic Aperture Radar (SAR) to monitor crops. In this research, machine learning algorithms are used to relate SAR parameters from RADARSAT-2 fully polarimetric and Sentinel-1 dual-pol (VV-VH) Single Look Complex data to optical Normalized Difference Vegetation Index values. For four crops (canola, corn, soybeans, wheat) Random Forest Regressors and Least-squares Boosting were able to create strong models using multiple polarimetric parameters. Coefficients of determination (R
²
) ranged from 0.91 to 0.84 depending on the crop type, sensor and model. Errors for oats and barley were higher due to a more limited training dataset.