Accurate crop classification plays a critical role in agricultural monitoring and food security assurance. Effectively exploiting spatiotemporal information from multi-temporal remote sensing data remains a key challenge in crop mapping. This study proposes an improved neural network model, termed the (3+2) D Split-Attention Feature Pyramid Network ( (3+2) D SAFPN), which is built upon a hybrid 3D–2D Feature Pyramid Network ( (3+2) D FPN). The model integrates a 3D FPN to capture spatiotemporal crop dynamics, a 2D FPN to extract multi-scale spatial features, a split-attention (SA) mechanism to enhance inter-channel information interaction, and a focal loss function to improve learning performance on minority crop classes. Multi-temporal Sentinel-2 imagery acquired in 2024 was used to construct a plot-level NDVI time-series dataset for Talhu Town, Wuyuan County, Bayannur City, Inner Mongolia. The dataset was divided into training, validation, and test sets with a ratio of 6: 2: 2. Experimental results demonstrate that the proposed (3+2) D SAFPN model achieved overall accuracies of 89. 01% and 89. 06% on the test and validation sets, respectively, with Kappa coefficients of 0. 82 for both sets, outperforming the original (3+2) D FPN model. Furthermore, comparative experiments conducted on the public Munich dataset indicate strong generalization ability, with accuracy improvements of 2. 88% on the test set and 2. 44% on the validation set compared to the baseline model. The results indicate that the (3+2) D SAFPN model effectively integrates spatial, spectral, and temporal information from multi-temporal remote sensing imagery, providing a robust and high-accuracy solution for crop classification tasks. This approach shows strong potential for large-scale agricultural monitoring applications. The source code of the proposed model is publicly available at: https: //gitee. com/btgw/YicongSun/ree/ (3+2) D-SAFPNₜorch.
Sun et al. (2026) studied this question.