Recently, research utilizing machine learning algorithms provided within the Google Earth Engine(GEE) platform has increased substantially. In this study, low to medium satellite resolution satellite data provided by GEE through the application programming interface (API) and high-resolution satellite data not directly provided by GEE. Sentinel-2, KOMPSAT-3A, and Landsat 8, which are optical images that provide spectral information, and Sentinel-1 and KOMPSAT-5 data, which are radar-based SAR images that are not affected by clouds or atmospheric conditions, were used together. In addition, NDVI (Normalized Difference Vegetation Index) calculated using surface reflectance data Sentinel-2 and Landsat-8 was added, and land use/land cover (LULC) classification was performed using the Random Forest algorithm. The resulting LULC classification was evaluated by comparing the overall accuracy and Kappa coefficient, while the F1-Score was employed as a complementary performance metric. The SHAP method was then applied to interpret the classification model. As an explainable machine learning technique, SHAP is particularly suitable for analyzing complex models that incorporate numerous input variables and multiple target classes, and it can be more effective than deep learning approaches that primarily focus on single-object extraction. Based on the classification outcomes, SHAP analysis was conducted to identify the most influential features for each land-cover type, demonstrating its usefulness in understanding the contribution of multi-sensor inputs. This study highlights that explainable machine learning techniques, such as SHAP, can serve as powerful tools for interpreting and monitoring surface environmental conditions, particularly when working with multi-sensor high-resolution satellite imagery.



Keywords : KOMPSAT-3A, KOMPSAT-5, Google Earth Engine, SHAP, Image classification, Random Forest