The research initiatives envisaged by the PNRR (Italian National Recovery and Resilience Plan) include the creation of innovation ecosystems to promote collaboration between universities, research centers, and local institutions with a focus on territorial integration and sustainability. The NODES Project (Nord-Ovest Digitale E Sostenibile) is part of this research. In this context, the Laboratory of Geomatics of the University of Pavia, in collaboration with other partners, deals with the study of the suitability maps for the renowned Pinot Noir wine. To achieve this, we considered different thematic input layers: elevation, slope, aspect, soil depth and type, Land Use Land Cover maps, NDVI, and current and forecast climatic aspects. An important thematic layer is concerned with the air temperature, which requires high spatial and temporal resolution. In the selected study area, the Lombardy Region has some accurate and reliable weather stations with high temporal resolution but low spatial resolution (7 stations in 648.5 square kilometers, i.e., one every 92 square kilometers). In addition, we considered Land Surface Temperature (LST) MODIS maps: these maps have good spatial resolution but present some voids and low temporal resolution. From the first evaluations made, the temperatures reported by MODIS are not always in excellent agreement with the ones from monitoring stations. To evaluate MODIS as a data source, we decided to use Kriging spatio-temporal interpolation. Starting from multitemporal MODIS data matrices, we interpolate them to estimate the temperature of the weather stations, in order to compare the estimation with the real weather station data, thus allowing the validation of MODIS 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.