<p>Accurate delineation of the geographic scope of agricultural production at the administrative level is a fundamental requirement for national policymakers. Estimating potential yield without spatio-temporal information might lead to inaccuracies. It is important to consistently conduct crop field mapping to categorize agricultural production estimates effectively. This study aims to devise methodologies utilizing remote sensing data to map irrigated crops in small-scale farms and diverse cropping systems inside Pakistan. The analysis used three Landsat OLI images within the Toba Tek Singh Tehsil, Pakistan. The ISODATA (Iterative Self-Organizing Data Analysis Technique) classifier was employed to differentiate between vegetated and non-vegetated regions. The study employed supervised classification and NDVI (Normalized Difference Vegetation Index) thresholds to map agricultural area features. Afterward, it examined and compared the effectiveness of both methods. Overlay analysis was conducted by implementing basic additions to enhance accuracy and evaluate classification performance. A set of land cover maps comprising nine distinct land use classes was created. The results of the accuracy evaluation revealed that the error indications accounted for 12% of the final product, while the overall accuracy of the final product was determined to be 78.03%. The findings of this study have significant implications for agricultural policymaking, particularly in regions with small-scale and heterogeneous cropping systems, and contribute to advancing remote sensing practices for crop monitoring and classification.</p>