Spatiotemporal Modelling of Ground-Level Air Temperature in an agricultural context: Rigorous Evaluation of LST Modis and Landsat-8 Imagery Data

Ground-level air temperature (Tair) is an essential variable for climate monitoring, agricultural management, and hazard prevention. Conventional ground-based measurements often fail to capture the fine-scale spatial variability, especially in regions with complex terrain. Land Surface Temperature (LST) remote sensing offers a complementary solution, providing spatially continuous and temporally frequent observations. This study evaluates the potential of MODIS and Landsat-8 LST products to estimate Tair in a heterogeneous agricultural landscape. We developed spatiotemporal regression models linking satellite-derived LST to ground observations from meteorological stations over the five years 2018–2022. MODIS data provided high temporal coverage through 8-day composites, while Landsat-8 offered higher spatial resolution LST via the Statistical Mono-Window algorithm. The models were validated using Leave-One-Out Cross-Validation, achieving high predictive accuracy for MODIS-based Tair estimation (R2 = 0.981, RMSE = 1.1 °C), whereas Landsat-8 captured finer spatial variability (R2 = 0.859, RMSE = 3.4 °C). Our results demonstrate that integrating multi-resolution LST products enables accurate, dense mapping of Tair, supporting operational forecasting for precision agriculture. The study also discusses limitations related to land-cover heterogeneity, temporal representativeness, and potential extensions using spatial correlation methods or radar-derived crop-structure information.