The Chinese 10 m monthly leaf area index of key growth stages of winter wheat in 2020-2025

The retrieval of Leaf Area Index (LAI) from satellite imagery is crucial for monitoring global carbon cycles and food security. Methods for estimating LAI based on spectral reflectance and Vegetation Indices (VI), known as VI-LAI models, are widely used for LAI retrieval due to their convenience. However, the VI-LAI relationship can vary with changes in Leaf Chlorophyll Content (LCC), resulting in low generalizability of the corresponding VI-LAI models. To address this issue, this study proposes a novel approach called the Difference Combination between Spectral Indices (DCSI), which combines existing VIs and derives a new index for generating LAI models, i.e., Sentinel-2 Modified Red Edge Position (S2MREP). The validation results based in-situ measurement data show that, the developed model appears high accuracy (R²=0.72, RMSE=0.90, RRMSE=23.61%) and robustness. It is against various confounding factors such as leaf chlorophyll content, canopy structure, and field background. With Google Earth Engine (GEE) cloud processing platform, the model can apply to generating national monthly winter wheat LAI maps.Note: This dataset contains the monthly spatial distribution of winter wheat LAI over the key growth stages (from tillering to maturity stages, i.e., from January to May), across China and from 2020 to 2025, with 10 m spatial resolution. It could apply to the yield prediction and the analysis of fertilizer and water management. For storage convenience, the values in the dataset have been scaled by a factor of 100. Users should divide the values by 100 during application.