## 5 Conclusions  
This study proposed a methodology to produce LULC maps at a lesser cost and in a quick manner by using three ML algorithms (KD-Tree, RF, and K-NN) and two satellites (Landsat-8 and Sentinel-2A). Timely updated maps can help the local authorities with better resource management and land-use policy decisions. The proposed methodology todevelop the LULC maps with remote sensing indices can be leveraged by researchers to determine the spatial-temporal changes of LULC due to human activities. The results of this study demonstrated the potential of remote sensing indices to limit the need for ground truth data for LULC mapping. This would lower the labour cost, time, and resources required to generate LULC maps.  
In this study, training samples for four classes, forest, agriculture, urban, and barren land, were created on behalf of indices, and these training samples were used in conjunction with the ML algorithms for LULC mapping. The prepared LULC maps based on this proposed methodology showed promising results when they were validated with ground truth data. The six LULC maps, produced by running the three ML algorithms using the same training data for the two sources of imagery, were subjected to an accuracy assessment to determine the effectiveness of the ML algorithms.  
Results from the study demonstrated that K-NN achieved average kappa coefficients of 82 and 74% and high overall accuracies of 86 and 81% for Sentinel-2A and Landsat-8, respectively. In comparison, the KD-Tree had average kappa coefficients of 80 and 78% and overall accuracies of 85 and 84% for Sentinel-2A and Landsat-8. Random forest achieved the highest average kappa coefficients, at 83.3 and 73.3%, and highest overall accuracies, at 92 and 85%. for Sentinel-2A and Landsat-8 data, respectively, compared to K-NN and KD-Tree.  
Further research should be conducted in two tasks: (1) the evaluation of this methodology on satellite images with a higher resolution as well as refining the data by training samples for subclasses of crops such as potato, wheat, rice, maize, and grasses and (2) the quantity and quality of training samples have an impact on land cover classification. By assuring quality and increasing the training sample size, classification accuracy can be enhanced. The ideal combination of training sample size will also be researched in the future.  
Conceptualization, [PERSON], [PERSON], [PERSON] and [PERSON]; Data curation, [PERSON]; Formal analysis, [PERSON]; Funding acquisition, [PERSON] and [PERSON]; Investigation, [PERSON]; Methodology, [PERSON]; Project administration, [PERSON]; Resources, [PERSON]; Supervision, [PERSON] and [PERSON]; Validation, [PERSON] and [PERSON]; Visualization, [PERSON]; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the following grant source: Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants Program [RGPIN-06295-2019].  
Not applicable.  
This website, [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 30 May 2022), provides access to the data used in this study.  
The authors would like to thank the Dalhousie University Agricultural Mechanized Systems Team and the University of Prince Edward Island's Precision Agriculture Research Team for their help and assistance with this project.  
The authors declare that they have no conflict of interest.