Crop classification using object-oriented method and Google Earth Engine

Abstract

Agriculture crop monitoring in real-time is crucial in formulating effective agricultural practices and management policies. The primary goal of the investigation is to explore how the utilization of Sentinel-1 data and its fusion with Sentinel-2 impact crop classification accuracy in a fragmented agricultural landscape in the Yavatmal District of Maharashtra, India. Pixel based classification and object-oriented classification approaches were implemented on Google Earth Engine (GEE), and obtained results were compared for different combinations of optical and microwave features. The research revealed that the object-based technique performed better than the pixel-based approach, with a 3.5% increase in overall accuracy. For 2022, crop-type mapping was generated with overall accuracies varying from 85.5% to 61% and a kappa coefficient between 0.77 and 0.37. These overall accuracies imply that joint use of optical and radar data has given a 24% improvement in overall accuracy compared to use of only optical data. In addition, the temporal change in the backscatter coefficients and different vegetation indices for different crops were examined over crop growth cycle. This work demonstrates the fusion of Sentinel-1 and Sentinel-2 data to classify wheat, chickpea, other crops, water and urban areas.