Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations
Accurate and timely winter wheat yield prediction is critical for effective agricultural management and food security. This study used the World Food Studies (WOFOST) model, a widely adopted crop growth simulation model, to dynamically simulate winter wheat yield under various growth scenarios to pr...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2024.2443470 |
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| Summary: | Accurate and timely winter wheat yield prediction is critical for effective agricultural management and food security. This study used the World Food Studies (WOFOST) model, a widely adopted crop growth simulation model, to dynamically simulate winter wheat yield under various growth scenarios to produce a simulated dataset. Based on this dataset, custom yield estimation models were developed based on available remote sensing data. Validation with field-measured and county-level statistics demonstrated a robust and spatially extensive capability for accurate yield estimation, with R2, RMSE, and MRE values of 0.57, 424.80 kg/ha, and 6.57% at the plot level, and 0.58, 345.53 kg/ha, and 4.93% at the county level, notably improving on traditional field-based methods (R2 = 0.03–0.46) that primarily rely on limited field surveys and statistical models. Model simplification showed that accuracy decreased when fewer remote sensing images were used, yet achieved reasonable estimates (two temporal phases: R2 of 0.41/0.40 at plot/county level). Findings highlighted that data collection during key growth stages is essential for accuracy, and that a dataset of at least 5,000 records suffices for reliable results. This study offers important insights and direction for enhancing yield prediction with efficient data acquisition and modeling strategies in large-scale applications. |
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| ISSN: | 1753-8947 1753-8955 |