## 6 Conclusions  
This study concerns the analysis of the correlation between Land Surface Temperature (LST) retrieved from MODIS satellite data and air temperature (Tair) measured by ARPA monitoring stations in the Oltrepo Pavese area. Since the Oltrepo Pavese is renowned for its significant wine production and extensive vineyards, understanding the impacts of climate change on viticulture is of great importance to adopting solutions to adapt viticulture to evolving climate conditions. This can be achieved thanks to suitability maps that can be useful to identify areas where environmental conditions allow optimal vine growth. Among the thematic layers considered, such as land morphology and land use, bioclimatic indices play a crucial role. Temperature and climatic patterns directly influence wine production; therefore, we focused on air temperature, which is the key data for evaluating the Winkler Index that is useful to guide decisions related to vineyard cultivation. Our goal is to create annual raster maps of the Winkler Index. By exploiting these annual raster layers, we aim to capture temporal variations in the Winkler Index, ensuring a complete understanding of trends over time. While monitoring stations measure air temperature with high temporal resolution, their spatial distribution is sparse. Therefore, it is essential to increase the density of temperature data. For this reason, we investigated the possibility of using LST as an input variable to estimate Tair [34], to obtain a higher spatial resolution than the in-situ measurements provided by ARPA monitoring stations. The 8-day mean of Tair can be estimated using remote sensing techniques, as from MODIS LST products we can retrieve four temperatures (day and night LST temperature from Terra and Aqua MODIS satellites). We were able to determine that there is a strong correlation between LST and near-surface air temperature (Tair), although the two temperatures have different physical meanings and responses to atmospheric conditions [34]. By applying a linear regression model on the initial dataset, we estimated air temperature from MODIS LST data with a temporal granularity of 8 days and a spatial granularity of 1 km\({}^{2}\). This method has potential application in regions with low density of monitoring stations, in search of sustainable solutions to adapt viticulture to climate change, including mapping of vocational features, evaluation of alternative crops, and vaporization of waste materials. Furthermore, this approach can be useful in cases where monitoring stations do not cover the entire time span considered. The simplicity of the employed models, the accessibility of input data at a global scale, and the good performances obtained suggest that this methodology could be applied to other regions. Future developments could explore the improvement of Tair estimates from remote sensing data by applying alternative regression algorithms incorporating both temporal and spatial factors and evaluate other remote sensing temperature data such as those from the COPERNICUS program. Writing--original draft, M.T.R. ; Writing--review & editing, M.F. ; Supervision, V.M.C. All authors have read and agreed to the published version of the manuscript. Funded by the European Union--NextGenerationEU, Mission 4 Component 1.5--ECS00000036--CUP F17G22000190007. Informed Consent Statement: Not applicable. Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The present paper has been prepared in the framework of PhD Course XXXVIII cycle. The authors declare no conflict of interest. Figure A4: Comparison between daytime and nighttime temperatures detected from MODIS Aqua and Terra satellites, interpolated to the Santa Margherita di Staffora Casanova ARPA Monitoring Station coordinates (ID 8202).