Mapping Coconut Plantations in the Brazilian Semi-Arid Region Using Machine Learning Techniques and Sentinel Imagery

Coconut farming in the Brazilian semi-arid region, especially in the state of Ceará, plays a significant economic role but faces challenges such as the limited use of modern agricultural technologies. The use of remote sensing techniques, through both multispectral satellite imagery and radar data, offers an opportunity to efficiently, quickly, and accurately monitor crop conditions, promoting more effective and sustainable management. This study aimed to map coconut plantations in the municipality of Paraipaba, in the state of Ceará, using machine learning techniques. Multispectral images from Sentinel-2 satellites were used, covering the period from January 1, 2024, to March 8, 2025, and radar images from Sentinel-1 satellites, from January 1, 2023, to May 30, 2024. The study employed Support Vector Machine (SVM) and Random Forest (RF) classifiers, with RF demonstrating superior accuracy in both multispectral and radar images. The RF classifier achieved an accuracy of 97.8% and a kappa coefficient of 94.2% with multispectral images, outperforming SVM, which yielded an accuracy of 86.3% and a kappa of 71.9%. For radar imagery, RF achieved a global accuracy of 98.6% and a kappa of 93.3%. This study highlights the effectiveness of machine learning techniques, particularly RF, for the accurate and efficient mapping of coconut plantations using Sentinel-1 and Sentinel-2 satellite imagery