AI-Driven Weather Data Superresolution via Data Fusion for Precision Agriculture

Accurate field-scale meteorological information is required for precision agriculture, but operational numerical weather prediction products remain spatially coarse and cannot resolve local microclimate variability. This study proposes a data-fusion superresolution workflow that combines global GFS predictors (0.25°), regional station observations from southern Moravia (Czech Republic), and static physiographic descriptors (elevation and terrain gradients) to predict 24 h ahead 2 m air temperature and to generate spatially continuous high-resolution temperature fields. Several model families (LightGBM, TabPFN, Transformer, and Bayesian Neural Fields) are evaluated under spatiotemporal splits designed to test generalization to unseen time periods and unseen stations; spatial mapping is implemented via a KNN interpolation layer in physiographic feature space. All learned configurations reduce mean absolute error relative to raw GFS across splits. In the most operationally relevant regime (unseen stations and unseen future period), TabPFN–KNN achieves the lowest MAE (1.26 °C), corresponding to an ≈24% reduction versus GFS (1.66 °C). The results support the feasibility of an operational, sensor-infrastructure-compatible pipeline for high-resolution temperature superresolution in agricultural landscapes.