Accurate crop map is critical for agricultural monitoring and related decision-making. Although many algorithms were adopted for crop classification, the performance of individual classifier varied with study area and data used. Aiming to address this issue, a novel ensemble learning (EL) framework was developed, which adopted objective weighting methods to assign weights to five widely used classifiers, including K-nearest neighbor, support vector machine, random forest, back propagation neural network and convolutional neural network. Four study sites in the United States were selected to examine the performance of the proposed EL framework. Time series of normalized difference vegetation index derived from Sentinel 2 multispectral instrument images were used as the input features for crop classification. Two modified objective weighting methods, termed modified entropy method (mEN) and modified coefficient of variation method ( mCV), were proposed to determine the weights of base classifiers. The probability outputs of base classifiers were combined with weights to determine the final label. The results showed that weights assigned by modified weighting methods were more reasonable than those by original weighting methods in multi-classifier ensemble. The combination of mEN and mCV (mEN mCV) further amplified the weight difference of base classifiers, and achieved an improved performance than single weighting method. Compared with five base classifiers, overall accuracy achieved by mEN mCV with F1-score as input ( mEN mCV F) was increased by 1.12 ~ 6.45 percentage points, 0.75 ~ 3.98 percentage points, 0.45 ~ 2.70 percentage points and 1.15 ~ 2.50 percentage points at four sites, respectively. The advantage of the proposed EL framework over unweighted ( i. e. , majority vote, probability fusion) and accuracy-weighted methods was that both classification accuracy and stability of base classifiers were considered, thus resulting in a higher performance. These results indicated that the proposed EL framework had potential in improving the accuracy of crop classification.