Machine Learning-Based Approach for Tillage Identification Using Sentinel-1 Data

Agriculture tillage is a fundamental practice in farming that involves preparing the soil for planting crops. It has been an essential technique used by farmers for centuries to improve soil conditions, increase crop yields, and enhance overall agricultural productivity. While tillage information can be acquired through manual field data collection, implementing this approach consistently and systematically over a wide area poses considerable challenges. Instead, remote sensing methods offer a viable option to comprehensively, promptly, and affordably investigate tillage activities. Hence, there is significant value in embracing a remote sensing approach to consistently and methodically monitor tillage practices across various fields. The objective of this research was to determine different types of tillage surfaces by analyzing the radar backscatter response received from the ground. The study used data from the Sentinel-1 satellite, specifically the Interferometric Wide-swath (IW) Ground Range Detected (GRD) dataset, which provided radar measurements in both VV and VH polarizations. To monitor tillage, we utilized supervised classification methods, namely decision tree (DT), random forest (RF), and support vector machine (SVM). Among these classifiers, the RF has the highest test accuracy of 0.86. The obtained results were validated using the ground observation data and found encouraging.