Abstract: It is significant for the market management and regulation of local government to accurately extract wolfberry planting areas in the Qaidam Basin using remote sensing technology. Taking the Nuomuhong Farm, a typical wolfberry planting area, as an example, this study selected Landsat8 OLI and GF-1 WFV images to construct the time-series NDVI/EVI data of the crop growth period. Then, this study employed four novel ensemble learning classifiers (i.e., LightGBM, GBDT, XGBoost, and RF) and two widely used machine learning classifiers (SVM and MLPC) to classify wolfberry planting areas. The results show that: ① Relatively high classification accuracy were obtained using LightGBM (90.4%), GBDT (90.4%), XGBoost (89.31%), and RF (86.96%). Most especially, LightGBM-EVI yielded the highest overall classification accuracy (91.67%), with a Kappa coefficient of 0.90; ② Enhanced vegetation index (EVI) is more sensitive in the middle-late stage of the wolfberry growth period. For the same classifier, better mapping effects of wolfberry planting areas can be obtained when time-series EVI data were used; ③ Data redundancy can be further reduced while obtaining high classification accuracy by determining the optimal temporal features of NDVI/EVI classification using the feature importance scores of the GBDT, XGBoost, and RF classifiers.