## 2 Materials and Methods
### Input Data  
To study the occurrence of frosts in the Valencian Community, we utilized MSG images collected from 2004 until 2023. This satellite constellation captures images of the study area every 15 min; however, we opted to consider only one image per hour to streamline the dataset while maintaining high representability.  
In total, 85,208 images were processed, comprising 24 images for each day (the first image obtained per hour) for the months of January, February, March, April, November, and December across the entire study period. We focused on these months because they are when frost occurrences are most likely and can potentially damage the plantations. All images were sourced from the open-access service Satellite Application Facility on Land Surface Analysis (LSA SAF; [[https://lsa-saf.eumetsat.int/en/data/products/](https://lsa-saf.eumetsat.int/en/data/products/)]([https://lsa-saf.eumetsat.int/en/data/products/](https://lsa-saf.eumetsat.int/en/data/products/)), accessed on 1 September 2023).  
Figure 1: Satellite view of the study area, with a detailed inset of the Iberian Peninsula in the upper-left corner.  
From these images, we selected the MSG LST band of the MLST product, which provides information on LST at a spatial resolution of 3 km. This product is already provided with clouds masked. We decided not to perform downscaling to enhance spatial resolution, as is sometimes undertaken in monitoring frost in cultivated fields [16]. We determined that such downscaling would introduce significant errors when applied to the large volume of images in our study and would not serve our purpose of monitoring frosts at a regional level.  
The 3 km spatial resolution of MSG may present certain challenges when detecting frost events in heterogeneous landscapes. This resolution may mask localized variations in land surface temperature (LST) caused by microclimatic factors such as topography, vegetation cover, or land use. Consequently, frost events occurring in smaller, isolated regions, such as valleys or shaded zones, might not be fully captured. However, MSG was selected due to its high temporal resolution and the large number of images it provides within a single day, which is essential for studying temporal dynamics.  
Although there are many variables that can influence the frost process and other important factors, such as precipitation, which also need to be considered, as mentioned in the introduction, the decision to conduct the analysis using only temperature data was primarily due to the large dataset involved. When determining the focus of the study, we concluded that hourly temperature data spanning 20 years would yield more relevant results, which was not the case for precipitation data.  
Regarding phenology, the increase in temperatures has, in some cases, led to a longer growing season [13; 14]. When analyzing this over an extended period, it became unfeasible because the temperatures in the later years of our study period were significantly higher than in the earlier years. For this reason, we focused solely on the perspective of temperature and how it should be considered in the study and planning of citrus cultivation.  
For this reason, this study is primarily based on a thermal perspective, as analyzing 20 years of temperature data allows us to observe the temperature increase over the period and how this influences the primary variable for frost events, which is sub-zero temperatures.