Assessment of the Performance of Sentinel-2A MSI and Landsat 9 OLI Images in Land Cover/Use Classification by Comparing Machine Learning Algorithms: A Case Study of Soma District, Türkiye

The combination of rapid, uncontrolled population growth and economic and industrial development has significantly accelerated land use/land cover (LULC) changes. Assessing these changes is one of the most effective ways to understand and manage land transformation. The advancement of remote sensing technology and increased accessibility to satellite data have made it more feasible to produce accurate and up-to-date LULC maps through the development of classifier algorithms. This has enabled better assessment and management of ecosystem and land use changes. The main objective of this study is to evaluate the performance of four machine learning algorithms—RF, SVM, CART, and GTB—using Sentinel-2 and Landsat 9 satellite images for the Soma district of Türkiye, on the Google Earth Engine (GEE) platform. In the study, a kernel function was applied to the SVM algorithm. Downloaded satellite images were visually inspected, and Google Earth Pro images were utilized to create training and test samples. Sentinel-2 and Landsat 9 images were classified using these training data and machine learning algorithms on the GEE platform. In the evaluation of the results, an error matrix was generated for the classified images, using the test samples for validation. The evaluation showed that the overall accuracy of the SVM algorithm, using the kernel function, was 92.6% for Sentinel-2 and 87% for Landsat 9, placing it third in terms of accuracy. The GTB algorithm provided the highest overall accuracy, with 94.4% for Sentinel-2 and 88.3% for Landsat 9. The RF algorithm achieved 93.2% accuracy for Sentinel-2 and 87% for Landsat 9, matching the accuracy of SVM for Landsat 9. CART demonstrated the lowest performance, with 86.4% accuracy for Sentinel-2 and 91.4% for Landsat 9. Additionally, Sentinel-2 imagery performed better than Landsat 9 across all algorithms due to its higher spatial resolution and spectral characteristics. This study provides valuable insights for local and provincial planners, authorities, and decision-makers regarding proper land management and the production of reliable LULC maps, especially in mining regions.