Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul

In recent years, Kabul city's rapid urbanization has adversely affected the urban land cover, such as surface water bodies and croplands. Surface resources are threatened due to overpopulation in city either qualitatively or quantitatively, also croplands being lost with development of activities through city. To monitor assess changes accurately, we classified area using satellite images both Landsat-8 Sentinel-2 compared their findings. The Support Vector Machine classifier was applied multi-senor data classify four different categories same training sites samples period. All procedures were conducted Google Earth Engine (GEE) cloud platform. reflectance bands satellites used for classification. Confusion matrixes created reference points classification compare results determine best approach cover. Results show that overall accuracy 94.26% while it 85.04% Landsat-8, similarly, Kappa coefficient calculated 91.7% 78.3% respectively.