Abstract: [Purposes] To address the challenges of crop identification in hilly regions caused by complex terrain and fragmented fields,this study proposes an efficient intelligent crop identification method. [Methods] By integrating high-resolution Planet satellite imagery and UAV imagery,a “satellite-UAV”collaborative observation system was constructed.Precise samples are generated using UAV imagery.Based on multi-temporal data,a fusion of a two-dimensional convolutional neural network (CNN/U-Net)and a three-dimensional temporal network (3D-CNN/ConvLSTM)is employed to jointly explore the spatial-spectral-temporal features of crops,enabling intelligent crop identification. [Findings] The model achieved an overall accuracy of 87.51%(Kappa coefficient： 0.831).Wheat identification performed the best with a mapping accuracy of 96.32%,followed by rice and maize (>88%),while rapeseed reached 86.44%.Crop distribution exhibited distinct altitudinal characteristics： rice is concentrated in valleys below 360 m,whereas maize is widely distributed across hilly areas between 250～550 m. [Conclusions] The proposed method provides a reliable technical pathway for precise crop monitoring in complex terrain,offering practical value for advancing precision agriculture and regional agricultural management．

Key words: high-resolution remote sensing, UAV aerial survey, crop identification, deep learning, hilly regions