Crop Type Mapping in Smallholder and Complex Agricultural Landscapes Using Multi-Temporal Sentinel-2 Imagery on the Google Earth Engine Platform
This study demonstrates that a hybrid framework integrating multi-temporal Sentinel-2 imagery, topographic data, and Random Forest classification within the Google Earth Engine platform can achieve high-accuracy crop mapping (95% overall accuracy) in complex smallholder landscapes characterized by intermingled orchards and annual crops.