Field-scale irrigated winter wheat mapping using a novel cross-region slope length index in 3D canopy hydrothermal and spectral feature space

Understanding the spatial and temporal distribution of irrigated cropland at the field scale is essential for managing irrigation water use and addressing the water-food nexus. While global and regional irrigation products exist, they often classify irrigated crops based on machine learning principles, where irrigated crops outperform rainfed ones. However, these methods typically lack mechanistic representation and are rarely applicable at the field scale over long time series. Additionally, identifying irrigated cropland in dual-season systems poses challenges due to temporal heterogeneity, leading to potential misclassification. To address these issues, we constructed a 3D canopy feature space including hydrothermal characteristics (1-precipitation/P, 2-actual evapotranspiration/AET) and spectral characteristic (3-NDVI). This approach is based on two mechanisms: the impact of irrigation on water vapor cycling and its role in promoting crop growth. We introduced a novel cross-region Slope Length Index (SLI) to map irrigated and rainfed crops at the field scale. Our method involved downscaling NDVI and AET using spectral fusion techniques (STF) on Google Earth Engine (GEE), followed by fitting a robust rainfed line (AET = −125.41 + 0.84 × P, R2 = 0.70) at the provincial scale, and calculating the SLI. Then A case of irrigation map (Irri_HNP) was generated by a threshold for crop water supply and demand, achieving ≥ 38 % accuracy improvement on overall accuracy (OA = 0.973) compared to existing products. The SLI method also exhibited strong stability when generalized to the national scope (AET = −74.41 + 0.82 × P, R2 = 0.73), maintaining robustness in both drought and humid years (AET = −177.08 + 0.82 × P, R2 = 0.69). The method’s scalability and transferability have been rigorously validated across diverse regions and environments, spanning from provincial to national scales. This validation achieved an OA of 0.922, demonstrating robust performance under heterogeneous conditions. Furthermore, the framework provides actionable insights for field-scale crop management and agricultural water governance.