Ensemble Learning for Crop Monitoring from Multitemporal Optical and Synthetic Aperture Radar Earth Observations

Leaf Area Index (LAI) and biomass are the most critical biophysical parameters for crop monitoring. In this study, we used three ensemble-based methods, including Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), for crop parameter estimation and mapping of soybean and wheat in an agricultural region in Winnipeg, Canada. Various Vegetation Indices (VIs) and radar parameters were extracted from multitemporal multispectral Sentinel-2 images and Synthetic Aperture Radar (SAR) Sentinel-1 data. Feature selection was made, first, based on the correlation between extracted features and target biophysical parameters. Features with low importance were then removed based on the correlation between all features. The RF model has the lowest RMSE among the examined methods for dry biomass, wet biomass, and LAI for soybean. For wheat, XGB has the lowest RMSE for dry and wet biomasses, while RF led to LAI's highest accuracy.