Synergistic Use of Sentinel-1 and Sentinel-2 Images for in-Season Crop Type Classification Using Google Earth Engine and Machine Learning

Abstract

In-season crop type mapping can assist in early yield estimation, however, such data are not widely available. Currently available crop type maps mostly rely on either optical imagery or synthetic aperture radar (SAR), but there is a growing number of research that demonstrates the potential of synergistic optical and SAR data fusion. This research investigates the performance of machine learning approaches that account for both optical and SAR features to generate in-season crop type maps. Classification performance of three supervised machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), were tested. Experimental results demonstrate that the best performance for the in-season classification of corn and soybeans was obtained four months after the sowing (April – July) from the fusion of optical (Sentinel-2) and SAR (Sentinel-1) images. The in-season classification from SVM and RF demonstrated 81.2 % (overall accuracy) agreement with ground truth.