Soybean Yield Estimation Using Improved Deep Learning Models With Integrated Multisource and Multitemporal Remote Sensing Data

Accurate soybean yield estimation is critically imperative for modern agricultural systems amidst escalating global food security pressures, yet conventional methodologies are constrained for large-scale, high-frequency monitoring. To address this, an innovative deep learning framework, TransBiHGRU-PSO, is proposed for precise large-scale soybean yield estimation via effective fusion of multi-source, multitemporal remote sensing data, emphasizing robust and accurate estimation even with anomalous yield data. This framework synergistically integrates an optimized Bidirectional Hierarchical Gated Recurrent Unit (BiHGRU), a Transformer encoder, and a novel Greenness and Water Content Composite Index (GWCCI), with critical parameters optimized by Particle Swarm Optimization (PSO). County-level yield data from 12 U.S. states were used, supplemented by multi-temporal remote sensing datasets (MODIS surface reflectance, vegetation indices, environmental variables). Empirical analyses showed TransBiHGRU-PSO demonstrated improved estimation capability and generalizability compared to multiple benchmark models. Notably, with anomalous yield data retained, the model achieved solid test set performance (coefficient of determination (R²) of 0.71, root mean square error (RMSE) of 4.2812 bushels/acre). Compared to the best traditional machine learning model (SVR), R² increased by 52.96% and RMSE decreased by 26.05%; and relative to the best deep learning baseline model (LSTM), R² and RMSE improved by 7.04% and 7.04%, respectively. Furthermore, validation of interannual stability (2008–2018, anomalies retained) revealed a mean R² of 0.70 and mean RMSE of 4.4701 bushels/acre, affirming its consistency under complex real-world conditions. This TransBiHGRU-PSO algorithmic framework, combined with multi-source and multitemporal remote sensing data, offers a valuable exploration for large-scale soybean yield estimation.