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A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover From 2001-2023 Using MODIS Satellite Data Over Asia
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DOI：10.1109/JSTSP.2026.3660045.png)
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Abstract
En 中文
Wheat is a staple crop in over 40 countries, with Asia accounting for more than 40% of global cultivation. Long-term mapping of wheat cover is critical for agricultural management and food security. However, existing wheat mapping products face a key limitation in spatiotemporal coverage: they either offer broad spatial coverage for a single or few years, or provide long time series that are confined to specific regions. To address this gap, we propose DeepMapping, a hybrid deep learning framework designed to generate a consistent annual fractional wheat cover product at 250 m resolution from 2001 to 2023 over Asia. Our framework integrates Convolutional Neural Networks (CNNs) and Transformer models to extract complementary spatial and temporal features. It processes multi-resolution data, including 250 m and 500 m MODIS reflectance, alongside GLASS Leaf Area Index and Fractional Vegetation Cover products. The model is trained with fractions derived from the 10 m resolution WorldCereal 2021 map and refined with ancillary information such as coarse-resolution crop products, land cover data, and agricultural statistics to enhance reliability. By learning the relationship between short-term, high-resolution labels and long-term, coarse-resolution MODIS observations, DeepMapping is efficiently used to generates annual wheat fractional cover product over Asia at 250 m from 2001–2023. Validation with 2,556 samples demonstrates overall accuracy of 84.1%, producer's accuracy of 87.8%, and user's accuracy of 70.07%. Comparison with national statistical data confirms high consistency (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula>: 0.880–0.943). DeepMapping offers a scalable solution for long-term, reliable agricultural mapping.
Keywords:
Agriculture
wheat fraction estimation
deep learning
MODIS
GLASS
remote sensing
Asia
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