NortheastChinaMaizeYield10m: A 10-m Resolution Maize Yield Dataset for Northeast China (2019–2024) Generated via a Mechanistically Interpretable, Label-free Framework

Accurate monitoring of crop yield is important for ensuring food security. Current yield estimation methods, such as machine learning models or the assimilation of remotely sensed biophysical variables into crop growth models, depend heavily on ground observations and involve significant computational costs. To solve these problems, a hybrid framework coupling the World Food Studies Simulation Model (WOFOST) and the Gated Recurrent Unit model (GRU) was proposed for maize yield estimation in Northeast China from 2019 to 2024. The model was trained exclusively on WOFOST-simulated data without using any ground-measured yield labels, and was subsequently transferred to Sentinel-2-derived leaf area index time series to generate spatially explicit yield maps.

This dataset provides 10 m annual maize yield maps for Northeast China from 2019 to 2024. Accompanying uncertainty layers (coefficient of variation) for 2023 and 2024 are also provided to support user assessment of local prediction reliability.

*** The data files are in ".tif" format

*** Temporal Resolution: annually

*** Temporal coverage: 2019–2024 (yield maps); 2023–2024 (uncertainty layers; remaining years under production)

*** Spatial Resolution: 10 m

*** Unit: kg/ha

*** Projection information: EPSG: 4326

*** Note: The CV values in the uncertainty layers have been scaled up by a factor of 10,000 for integer storage. Users should divide the pixel values by 10,000 to obtain the original CV values.

*** Data access: https://zenodo.org/records/19547014 (Hu et al., 2026)