Accurate large-scale crop mapping is critical for yield prediction, agricultural disaster monitoring, and global food security. Synthetic aperture radar (SAR), with its all-weather imaging capability, plays a vital role in remote sensing based on crop mapping studies. However, although feature-level fusion has been widely explored in remote sensing, existing VV and VH fusion approaches for rice mapping are still predominantly conducted at the data level and fail to adequately integrate their complementary information across the rice growth cycle, so the simplistic fusion methods yield features that are redundant or conflicting at field boundaries and in heterogeneous areas, thereby increasing classification errors. To address these challenges, this study proposes a novel spatiotemporal attention model (SimTA) for feature fusion to improve rice mapping. (1) A VV-VH feature-level fusion scheme is designed, integrated with a Content-Guided Attention (CGA) fusion method which effectively exploits the complementary information of the dual-polarized SAR data for achieving deep spatiotemporal dynamics fusion. (2) A Central Difference Convolution Spatial Extraction Conv (CDCSE Conv) Block is designed, enhancing sensitivity to edge variations in rice fields by combining standard and central difference convolutions. (3) To achieve efficient spatiotemporal feature integration across SAR time series, a Temporal–Spatial Attention (TSA) Block is developed, utilizing large-kernel convolutions for spatial feature extraction and a squeeze-and-excitation mechanism for capturing long-range temporal dependencies of rice time series. Extensive experiments were conducted by comparing SimTA with different models under five fusion schemes. Results demonstrate that feature-level fusion consistently outperforms other schemes, with SimTA achieving the best performance: OA = 91.1%, F1 score = 90.9%, and mIoU = 86.2%. Compared to the baseline Simple Video Prediction (SimVP), SimTA improves F1 score and mIoU by 0.8% and 2.1%, respectively. The CGA enhanced feature-level fusion further boosts SimTA’s performance to OA = 91.5% and F1 = 91.4%. SimTA bridges the gap between existing VV-VH deep fusion schemes and modern spatiotemporal modeling demands, offering a more accurate and generalizable approach for large-scale rice field mapping.