Agriculture is an important natural resource of our country, whereas coconut is one of the vital horticultural crops suitable for humid regions. Due to the impact of climate change and less awareness of farmers, major crop areas were converted into coconut plantations over the decades in the Western part of Tamil Nadu region. Even though numerous applications utilizing remote sensing tools have been attempted, regional mapping of coconut growing areas has not been studied at regular intervals. In this study, it is necessary to understand the dominance of coconut crop pattern changes in selected parts of the Coimbatore region. Multispectral-based Sentinel 2 data were used for unsupervised classification-based ISODATA algorithm and analysis. Three clusters were isolated as vegetation for NDVI mapping based on their spectral signature. The training data are assigned to import the maximum-likelihood algorithm of supervised classification to classify the heterogeneous coconut palm, and it is identified that nearly 310.99 km2 area belongs to coconut plantation and ground truth observation was carried out in assist of field mapping which contributes that overall accuracy of 83%. This finding showcases that the change in crop pattern will create awareness among the farmers about their high-yield income crops which can be extracted with a limited span of maintenance and less yield of water over different climatic conditions. The study comprises the importance and effectiveness of remotely quantifying the coconut plantation that will help stakeholders, agricultural statistics, NGOs, and farmers to detect the crop rate per unit of land that can assist in the implementation of appropriate agricultural practices to increase coconut palm production profits.