Land Cover Mapping Using an Unsupervised Classification Scheme: A Case Study from Ontario, Canada

High-resolution land cover classification is critical for monitoring environmental change and managing natural resources. This study presents a fully unsupervised framework that integrates Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical imagery at 10-meter spatial resolution. A cloud-native export protocol in Google Earth Engine (GEE) enables the generation of consistent, cloud-free, and snow-free seasonal composites across Ontario, Canada. A comprehensive feature engineering pipeline combines spectral indices, radar backscatter metrics, terrain derivatives from digital elevation models (DEMs), and temporal statistics to create a rich multi-sensor input space. Dimensionality reduction is performed using Sparse Principal Component Analysis (SparsePCA) and mutual-information based feature selection. Clustering is conducted using three complementary algorithms: centroid-based K-means, density-based Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), and reachability-based Ordering Points To Identify the Clustering Structure (OPTICS). Final land cover labels are assigned via a majority-voting ensemble, with prediction ties resolved deterministically using OPTICS. OPTICS is particularly effective for modeling heterogeneous landscapes due to its ability to detect clusters of varying density without requiring a global threshold. The resulting classification maps are validated against reference land cover data, demonstrating the scalability and effectiveness of the proposed label-free mapping approach.