## 4 Discussion  
The sentinel-2A and Landsat-8 presently operate at medium resolution at 10, 20, and 30 m. The resolutions of these two satellite bands are different. Before further processing, all Landsat-8 bands were resampled to 30 m resolution, while Sentinel-2A bands were resampled to 20 m resolution. This study presented the potential of different remote sensing indices to create the training samples for LULC mapping in PEI in conjunction with three ML algorithms. The Island population is increasing, and major land cover classes such as forest, agriculture, barren land, and urban will be affected. These rapid changes demand more effective methods to map land cover changes and conduct resource management analyses.  
The remote sensing indices, including DVI, NDVI, NDBI, UI, and NBIJ, were selected to highlight the agriculture, forest, barren land, and urban area. This approach to preparing LULC maps is much cheaper and faster than other classification methods traditionally used. Although, for some LULC classes, it is hard to find suitable remote sensing indices. For example, it is hard to distinguish between forests and agriculture using remote sensing indices. The NBIJ was used to overcome this problem because [6] documented that the NBIJ can highlight the soil composition at the pixel level, which helps distinguish between agriculture and forest (Figure 2). The results from the experiment also verified the validity of this proposed method.  
In the last step, we used the same algorithms processing conditions (same training and validation data sets) to compare the Landsat-8 and Sentinel-2A optimal data sets for LULC mapping. The comparison results indicated that the overall accuracy of each algorithm highly depends on the input data of the results. For example, the highest overall accuracy of RF 92% showed that RF offers the best classification results for Sentinel-2A, whereas KD-Tree and K-NN's overall accuracies were slightly decreased. Interestingly, RF also offers the highest overall accuracy 85% for Landsat-8; likewise, KD-Tree and K-NN's overall accuracy was slightly decreased compared to RF. All the results mentioned above proved that the outcomes of each classifier depend on the input data set. The results proved that RF is a suitable ML algorithm as compared to the KD-Tree and K-NN for land cover classification without considering input data sets. Therefore, it is necessary to compare the obtained results with the literature because it offers a realistic view of this study's results.  
For example, [PERSON] and [PERSON] [52] used the RF, SVM, maximum likelihood classifier for preparing the LULC map and achieved an overall accuracy of 87, 83, and 77%, respectively. Another study from [PERSON] et al. [53] prepared LULC maps with 13 classes using the K-NN algorithm and achieved an overall accuracy of 63%. These different results indicate that there are no clear rules for the acceptable accuracy for any land cover type, but it depends upon the user and adoptive methodology. In any LULC classification, errors are present in the form of estimation and prediction [54]. So far, no clear rules have been defined in the acceptable accuracy range because different users have different concerns about classification accuracy [55]. In addition, several factors influence the accuracy of classification, such as image quality, classifier, number of classes, and number sample size [51]. One study [51] that used Sentinel-2 data was for LULC mapping in Vietnam with RF, K-NN, and SVM algorithms, and it reported the highest accuracy by RF when the training vector size was appropriate to cover the study area. RF achieved a higher accuracy than the SVM by using Sentinel-1 data of the Brazilian Amazon [56]. These results indicate that Sentinel-2A and Landsat-8 data had satisfactory performance in LULC mapping. In [51], RF was recommended for LULC classification because of the ease in parameter selection in RF. The results of this study concur with the findings of [51].