This study evaluates the impacts of three booster types (two tree-based and one linear model) in extreme gradient boosting (XGBoost) for crop classification using multi-temporal PolSAR (Polarimetric Synthetic Aperture Radar) images. Ensemble learning algorithms have received great attention in remote sensing for classification due to their greater performance compared to single classifiers in terms of accuracy. Extreme gradient boosting is the regularized extension of traditional boosting techniques and could overcome the overfitting constrain of gradient boosting (a.k.a gradient boosting machine). Three types of booster which are linear booster, tree booster and DART (Dropouts meet Multiple Additive Regression Trees) booster were tested on XGBoost for crop classification. From the multi-temporal PolSAR data, two types of polarimetric dataset (linear backscatter coefficients and Cloude-Pottier decomposed parameters) were extracted and incorporated into the classification step. The impacts of polarimetric features for crop classification were also analyzed in detailed besides exploring the boosting types of XGBoost. Our experimental results demonstrated that tree booster and DART booster were found to be superior compared the linear booster in terms of overall classification accuracy for both polarimetric dataset. The highest classification accuracy (87.97%) was achieved by tree booster with linear backscatter coefficients. Furthermore, linear backscatter coefficients achieved higher performance with respect to Cloude-Pottier decomposition in terms of classification accuracy.