Optimizing Agricultural Land Use Mapping: Integrating Multiple Algorithms for Wheat Classification in Sindh’s Naushehro Feroze District, Pakistan

Wheat accounts for over 70% of Pakistan’s cereal production and serves as a staple for more than 200 million people, yet national crop monitoring systems remain reliant on infrequent, ground‐based surveys that lack spatial granularity and timeliness. Addressing this critical gap, this research advances the state of the art in crop‐type mapping by developing and validating a machine learning‐driven approach for high‐accuracy wheat classification in Pakistan using multitemporal Sentinel‐2 satellite observations. Ground‐truth reference data were integrated with phenologically sensitive vegetation indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), and Green Normalized Difference Vegetation Index (GNDVI), derived across the critical wheat phenophases from December to March. Four supervised classifiers—random forest (RF), support vector machine (SVM), classification and regression trees (CART), and minimum distance (MinDist) were implemented and comparatively assessed, with synthetic noise injected during training to enhance model generalization and mitigate overfitting. Rigorous evaluation using standard metrics (overall accuracy, precision, recall, and F1‐score) revealed that RF significantly outperformed competing algorithms, achieving an overall accuracy of 98%. The demonstrated efficacy of this approach provides a scalable, reproducible framework for near‐real‐time agricultural monitoring, with direct implications for improving crop inventory systems, informing food security policy, and enabling precision agronomy in Pakistan’s wheat‐dominant agroecosystems.