###### Abstract
This situation occurred in 2020 when the AAFC Annual Crop Inventory could not be completed in Nova Scotia due to COVID-19 travel restrictions. These restrictions prevented ground data collection, making it impossible to define some agricultural classes [12]. Although ground truth data remain essential to validate LULC maps, this situation highlights the importance of developing new methods that would limit the amount of ground truth data for LULC maps generation. The quantity of training and validation data is another factor that can influence the classification accuracy of ML algorithms [13]. For example, ML algorithms perform better with larger training data sets than with smaller data sets [14]. The minimal number of training samples should be 10 times the number of variables, according to a long-standing 'rule of thumb' in ML [15]. Unfortunately, there appears to be no comparable guidance in the literature regarding the minimum number of samples required for ML classification. According to the [16] classification method, the number of input variables and the size and spatial heterogeneity of the mapped area can each influence the number of training samples required in classification. Large, precise training data sets are required, according to the general conclusion [17]. However, in the realm of applied remote sensing, training and validation data were both costly and scarce [18]. As a result, most remote sensing studies used a single fixed data set to test the accuracy of ML algorithms [19; 20]. Several supervised and unsupervised algorithms have been developed to process satellite images for LULC classification. Unsupervised classification algorithms use automated statistical algorithms to separate LULC classes without training data. [PERSON] et al. [21] used an unsupervised algorithm to distinguish the land cover features in remotely sensed images. They used the K-means cluster algorithm to classify the LULC classes effectively. In another study, [PERSON] and [PERSON] [22] tested the maximum likelihood classifier for mapping the LULC classes under different scenarios. Supervised classification algorithms are very efficient at mapping LULC features. The supervised classification method involves the representative samples from each predefined class, followed by the training of algorithms to learn about LULC classes for efficient classification. Related work by [PERSON] and [PERSON] and [PERSON] and [PERSON] [23; 24] documentedthat supervised classification algorithms are likely to perform better than unsupervised classification algorithms. LULC maps require an appropriate classification algorithm to solve real-world problems with high accuracy [25]. Several studies have shown the potential of ML and statistical algorithms in LULC classification. For example, [PERSON] and [PERSON] [25] tested two supervised classifiers, namely maximum likelihood and minimum distance for LULC classification using Landsat imagery. The maximum likelihood classification of Landsat data had a higher accuracy than the minimum distance method. [PERSON] et al. [26] used the ground truth data to train the ML algorithms for LULC classification. Other studies by [PERSON] et al. [27] tested a support vector machine (SVM) and maximum likelihood algorithms on Landsat-8 imagery. The maximum likelihood classifier results were more accurate than the results of SVM. Over time, more advanced algorithms have been used in LULC classification, including decision trees (DT) and random forest (RF). [PERSON] and [PERSON] [28] tested RF, k-nearest neighbour (K-NN), and SVM using training sample sizes generated from Sentinel imagery. Results from the study showed that SVM had the highest accuracy and had the least sensitivity to the size of the training sample. However, K-NN and RF classifiers attained a higher accuracy with a large training size compared to SVM. The literature also reveals that every classification method performs differently depending on the types of satellites used to capture images. For example, [PERSON] et al.