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This research focuses on improving oilseed flax yield predictions in arid regions using advanced modeling techniques.

January 6, 2026Remote Sensing0 citationsOpen Access

Oilseed Flax Yield Prediction in Arid Gansu, China Using a CNN–Informer Model and Multi-Source Spatio-Temporal Data

XLXingyu LiGansu Agricultural University YLYue LiGansu Agricultural University BYBai YanNorth University of China

Key Points

This research focuses on improving oilseed flax yield predictions in arid regions using advanced modeling techniques.
Developed a CNN–Informer hybrid framework for yield prediction.
Utilized multi-source spatio-temporal data including remote sensing and meteorological variables.
Conducted experiments using historical yield data and various machine learning comparisons.
Achieved average R2 of 0.82, RMSE of 0.31 t/ha, and MAPE of 10.33%.
Found that remote sensing features significantly enhance predictive accuracy.
Validated model robustness with fivefold county-based spatial cross-validation.

Abstract

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN–Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN–Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R2 = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN–Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions.

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