Accurate and spatially reliable temperature data are crucial for effective agricultural management and climate adaptation. This study presents an innovative methodology for temperature correction using topographic factors and adiabatic models. Initial temperature information, sourced from the automatic weather stations (AWS) of the Dirección General de Aeronáutica Civil (DGAC), often has limited spatial representation, covering approximately 30 km in radius, however, significant temperature variations within this area require refined data for precise agronomic applications. This research proposes a method to spatialize and correct temperature data to enhance its reliability for agronomic use. More accurate spatial temperature maps are achieved by utilizing Digital Elevation Models (DEM) and applying corrections restricted to locations within 100 meters of elevation difference from the AWS. These corrections include wet and dry adiabatic lapse rates, which are applied appropriately. The corrected temperature data are then used to create detailed spatial maps, essential for modeling agronomic variables in changing environments. These maps enable better decision-making for irrigation scheduling, crop stress monitoring, and other critical agricultural practices. The methodology was tested in four distinct sites within the central-southern macrozone of Chile, specifically in the regions of O'Higgins, Maule, Nuble, and Biobío. The results from these case studies provided valuable insights into the applicability and accuracy of this technique in real-world agricultural settings, demonstrating significant improvements in climate resilience and sustainability. By improving the spatial accuracy of temperature data, this methodology supports more effective resource management and enhances the sustainability of agricultural operations. The findings highlight the potential of advanced topographic and adiabatic corrections to transform temperature data analysis, providing a valuable tool for climate-adaptive agriculture.