A framework combined stacking ensemble algorithm to classify crop in complex agricultural landscape of high altitude regions with Gaofen-6 imagery and elevation data

Mapping crop distribution using satellite technology is an effective approach for gaining information about food production over broad, regional scales. However, crop classification in high altitude regions from satellite platforms remains challenging, due to the spatial heterogeneity caused by the complex planting patterns. Moreover, the frequent cloud cover makes it difficult to collect time-series imagery for these regions. Thus, this study used a mosaic of single images of Gaofen-6 data to map the crop distribution in high altitude regions of Xining City and Haidong City prefectures of Qinghai Province, China. To improve the accuracy of the crop classification, random forest-recursive feature elimination (RF-RFE) was used to determine an optimal feature subset from existing spectral, texture and topographic features. Then, a two-layer stacking generalization ensemble model, incorporating Random Forest, XGBoost and AdaBoost, was trained. The results reveal that the stacking algorithm outperformed the other single classifiers, with overall accuracy higher than 85% (87.89% for the optimal feature subset and 85.38% for the original spectral band subset). In addition, the user’s and producer’s accuracies for wheat, rape and maize field all exceeded 90%. Elevation was the variable with the highest importance score, illustrating its importance in crop classification of high altitude regions. Overall, the framework, combining RF-RFE and a stacking algorithm, can improve the accuracy of the crop classification in high altitude regions.