###### Abstract  
Land use and land cover (LULC) classification maps help understand the state and trends of agricultural production and provide insights for applications in environmental monitoring. One of the major downfalls of the LULC technique is inherently linked to its need for ground truth data to cross-validate maps. This paper aimed at evaluating the efficiency of machine learning (ML) in limiting the use of ground truth data for LULC maps. This was accomplished by (1) extracting reliable LULC information from Sentinel-2 and Landsat-8 s images, (2) generating remote sensing indices used to train ML algorithms, and (3) comparing the results with ground truth data. The remote sensing indices that were tested include the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the normalized built-up index (NDBI), the urban index (UI), and the normalized bare land index (NBL). Extracted vegetation indices were evaluated on three ML algorithms, namely, random forest (RF), k-nearest neighbour (K-NN), and k dimensional-tree (KD-Tree). The accuracy of these algorithms was assessed with standard statistical measures and ground truth data randomly collected in Prince Edward Island, Canada. Results showed that high kappa coefficient values were achieved by K-NN (82% and 74%), KD-Tree (80% and 78%), and RF (83% and 73%) for Sentinel-2A and Landsat-8 imagery, respectively. RF was a better classifier than K-NN and KD-Tree and had the highest overall accuracy with Sentinel-2A satellite images (92%). This approach provides the basis for limiting the collection of ground truth data and thus reduces the labour cost, time, and resources needed to collect ground truth data for LULC maps. remote sensing indices; machine learning; ground truth data; LULC mapping; satellite imagery 2022113 created competition among big data scientists to increase classification accuracy with new classification algorithm approaches. According to [PERSON] et al. [5], population growth is a primary driving factor for LULC transformation. As a region's population grows, so does the demand increase for built-up space and food, while other land cover types, such as barren land, agricultural, and forest, decline. Food consumption is increasing due to the world's population having more than doubled between 1961 and 2016 [6]. Cropland covers about a third of the land, while grazing pastures and meadows cover the remaining two-thirds. Farmland area per capita has decreased substantially from 0.45 hectares per capita in 1961 to 0.21 hectares per capita in 2016. Urban areas expand outwards when agricultural and other natural land cover types are transformed into developed areas. Rapid urbanization triggered rapid economic growth and land-use changes, raising the demand for efficient natural resource management [7]. An increased population induced rapid urbanization, and the resulting economic growth and land-use changes heightened the demand for efficient natural resource management [8]. If the land cover changes are not continuously monitored with an increasing population, it may negatively impact the environment and resource management of the area [9]. An up-to-date and cost-effective method is required to prepare the LULC map for its resource management and planning and is helpful to meet the increased food demand. The national and regional LULC maps such as the Agriculture and Agri-Food Canada (AAFC) Annual Crop Inventory are produced yearly and help to understand the state and trends in agriculture production in Canada. The method commonly used for preparing these maps consists of collecting ground truth data and training a classifier [10]. Although this technique can be very accurate, the process of collecting reference data requires extensive planning, time, and important financial resources [11]. In addition, if ground truth data cannot be collected for a year due to unforeseeable events, the creation of LULC maps can be compromised and create a gap in the data.