This work analyzes the impacts of training sample size on the performance of supervised classification methods when coarse resolution imageries are employed for regional land cover mapping. We utilized FegnYun-3C composite imageries with 1km spatial resolution and random forest (RF) and support vector machine (SVM) algorithms that were trained and tested with five sets of reference datasets: 66/34, 69/31, 73/27, 76/24 and 79/21.The results show that the performance of the two algorithms increases with increasing the size of the training examples until a certain point, and achieves the maximum accuracy (0.86 for RF and 0.84 for SVM) when the ratio was 76/24. However, considering the 79/21 (train/test) ratio made no change in accuracy, implying increasing a training dataset beyond a certain limit has no effect. Moreover, despite the size of training samples employed, the RF outperformed the SVM in contrary to the claim that SVM yields a better accuracy in case of scarce training data by pervious studies.