关键词: 冬小麦, 产量预测, 物候阶段, 多源数据, 深度学习, CNN-LSTM-Transformer

Abstract:

[Objective] Winter wheat yield formation is jointly affected by multiple factors, including canopy structure, growth status, photosynthetic function, and meteorological conditions. The contributions of remote sensing, meteorological data, and photosynthetic information to yield prediction differ among phenological stages. However, the influence of information accumulation at different phenological stages on county-level winter wheat yield estimation remains unclear, which restricts further improvement in early prediction capability and agricultural interpretability of yield prediction models. Therefore, a phenological-stage progressive multi-source data fusion method was proposed for county-level winter wheat yield prediction, to improve the prediction accuracy of county-level winter wheat yield, clarify the influence of information accumulation at different phenological stages on yield estimation performance, and provide methodological support for dynamic regional yield prediction and monitoring of key growth stages. [Methods] 100 major winter wheat-producing counties of Henan province were selected as the study area. Remote sensing variables, meteorological variables, and solar-induced chlorophyll fluorescence (SIF)-based photosynthetic variables from 2013 to 2022 were integrated to construct a county-level multi-source time-series feature dataset covering the period from the tillering stage to the maturity stage of winter wheat. Among these variables, remote sensing variables were used to characterize canopy structure and crop growth status, meteorological variables were used to reflect heat and water conditions during the growth period, and SIF variables were used to characterize changes in crop photosynthetic function. Based on the winter wheat planting distribution mask and county-level administrative boundaries, county-scale time-series features of each variable were extracted. According to the developmental sequence of tillering, overwintering, greening, jointing, heading, anthesis, and maturity stages, the model input window was progressively expanded to form input sequences under different phenological-stage accumulation conditions. For model construction, a convolutional neural network-long short-term memory-transformer (CNN-LSTM-Transformer) model was adopted. The CNN was used to extract local variation features between adjacent time steps, the LSTM was used to capture continuous temporal dependencies among stages, and the Transformer Encoder was used to model global associations among time steps in the input sequence. To evaluate model performance, the CNN-LSTM-Transformer model was compared with random forest (RF), Extreme Gradient Boosting (XGBoost), and CNN-LSTM models. In addition, a leave-one-year-out validation strategy was used to analyze the interannual generalization ability of the CNN-LSTM-Transformer model under different year conditions. [Results and Discussions] In the test set based on county-level sample partitioning, the CNN-LSTM-Transformer model outperformed the RF, XGBoost, and CNN-LSTM models and achieved the best yield estimation performance. The coefficient of determination (R²), root mean square error (RMSE), and mean absolute percentage error (MAPE) were 0.827, 588.25 kg/hm², and 8.02%, respectively. Compared with the CNN-LSTM model, the R² of the CNN-LSTM-Transformer model increased by 0.054, while RMSE and MAPE decreased by 85.69 kg/hm² and 1.54 percentage points, respectively. These results indicated that introducing the Transformer Encoder helped enhance the model's ability to represent global multi-source time-series information. The phenological-stage accumulation analysis showed that model prediction accuracy continuously improved as growth-stage information gradually increased. Prediction accuracy was relatively low at the tillering and overwintering stages, whereas model performance improved most obviously from the greening stage to the heading stage. This indicated that multi-source information during this period could more fully reflect rapid canopy development, enhanced photosynthetic capacity, and the yield formation process of winter wheat. Therefore, the period from greening to heading was identified as the key stage for the rapid improvement of county-level winter wheat yield prediction ability. The residual analysis across yield zones showed that the CNN-LSTM-Transformer model alleviated, to some extent, the overestimation in low-yield areas and the underestimation in high-yield areas. The leave-one-year-out validation results showed that the proposed method maintained good prediction stability across different test years. The spatial distribution results further showed that the predicted yield could effectively reflect the county-level spatial pattern of winter wheat yield in Henan province, with lower values in the western region and higher values in the central and eastern regions. [Conclusions] Overall, the proposed phenological-stage progressive multi-source data fusion method effectively integrated remote sensing, meteorological, and SIF-based photosynthetic information, improved the prediction accuracy of county-level winter wheat yield, and revealed the influence of information accumulation at different phenological stages on yield estimation performance. The CNN-LSTM-Transformer model collaboratively represented local variation features, stage-wise temporal dependencies, and global long-term associations, thereby effectively characterizing multi-source time-series dynamic changes during winter wheat yield formation. The results showed that the period from the greening stage to the heading stage was the key period for the rapid improvement of county-level winter wheat yield prediction ability. This study could provide a reference for early winter wheat yield estimation, key growth-stage monitoring, and regional grain production management.

Key words: winter wheat, yield prediction, phenological stages, multi-source data, deep learning, CNN-LSTM-Transformer