This study estimates and maps PM10 concentrations in Makassar, Indonesia, using Landsat 8 imagery integrated with field-based measurements, and evaluates whether multi-band spectral information improves satellite-based PM10 estimation in a tropical coastal urban environment. Landsat 8 OLI/TIRS Collection 2 Level-1 imagery acquired on 27 March 2025 was processed through radiometric correction, atmospheric correction, and spectral reflectance extraction. Field-based PM10 measurements were collected from 30 observation points using the gravimetric method. The dataset was divided into 80% for model development and 20% for independent validation. Landsat 8 Bands 1–4 were used as predictor variables in multiple linear regression models, and model performance was evaluated using the correlation coefficient, coefficient of determination, and root mean square error. The results showed that Bands 1–4 had significant positive correlations with PM10 concentrations, with correlation coefficients ranging from 0.709 to 0.755. Model B, which combined Bands 1, 2, 3, and 4, produced the best performance, with R = 0.811 and R² = 0.658 during model development. Independent validation confirmed its superior performance, with R = 0.781, R² = 0.609, and RMSE = 33.1083 µg/m³. These findings indicate that satellite-based PM10 estimation in Makassar is feasible, but should be interpreted as a spatial approximation rather than a direct replacement for ground-based measurements. The model remains affected by optical sensor limitations, temporal differences between image acquisition and field measurements, cloud-related constraints, and the absence of meteorological variables. Practically, the resulting PM10 maps can help identify priority areas for air quality monitoring in cities with limited monitoring infrastructure. The originality of this study lies in demonstrating that a multi-band Landsat 8 configuration is more suitable for representing PM10 variability in a medium-sized tropical coastal city by capturing aerosol scattering, heterogeneous urban surfaces, and coastal environmental influences.