## 3 Methods
### Data Preprocessing  
Since input data have different spatial scales, all data were aggregated using an average function or resampled using the nearest neighbor to 1 km: Among the data used in this study, Roaddens, Poptens, elevation, and DN with less than 1 km spatial resolution were aggregated to the target resolution of 1 km using an average function. The smoothing effect caused by upscaling the original fine resolution data was negligible to the model performance (Supplementary Figure S1). The weather data at 5 km spatial resolution were simply downscaled to 1 km so that all 25 pixels in a 5-km grid have the same value. The VIF test was used to examine the multicollinearity between the input variables. VIF values \(>\) 10 generally indicate high multicollinearity between the variables, so the value of 10 is  
Figure 2: Process flow diagram of the proposed approach in this study.  
often used as a threshold for the VIF test [7; 44]. In this study, all VIF values were below 10, so all input variables were used to develop the models.  
Since this study was to develop a forest fire risk index, we approached it as a regression problem targeting forest fire risk rather than the binary classification of forest fire and non-fires. However, since in-situ data were provided only for forest fire occurrences, reference data for low forest fire risk were extracted in the areas where fires did not occur. To extract non-fire pixels (i.e., very low fire risk), the statistics of historical data were used. Monthly forest fire density was calculated using data of the past five years for each sampling year, resulting in a normalized value ranging from 0 to 1. For example, to extract non-fire samples in January 2015, the monthly forest fire density was calculated using January data from 2010 to 2014. If there were many fire occurrences in the surrounding areas of a location in the past 5 years, the location would have a high density. The density was then divided into four classes (25% each) using the percentiles for stratified sampling (i.e., non-fire samples were evenly extracted for each class). Note that the monthly forest fire density was only used as a supporting material to allocate the dependent variable value of non-fire samples and was not itself used as a dependent variable. The pixels with the high-density class (the top 25%) were excluded for extracting non-fire samples, while the pixels with the lower density classes had values from 0 to 0.2 (Table 2). The reason for assigning different values instead of 0 value to all non-fire pixels is to ensure the reliability of the non-fire pixel selection in the consideration of the forest fire pattern in recent years. In many studies on wildfire susceptibility, fires and non-fires were designated as 1 and 0, respectively, which often resulted in bimodal output (i.e., very high close to 1 and very low close to 0) with limited areas that have moderate susceptibility (e.g., 0.5-0.6) [9; 45]. If no forest fire has occurred at a region in the past years, the forest fire risk for the region might be statistically low, so it is desirable to allocate it to zero. However, the forest fire risk of the region might not be low if the region suffered from forest fires in the past considering the causes of the fires. Thus, we assigned low values to some non-fire samples with moderate forest fire density. The values of 0.1 and 0.2 were empirically determined by testing multiple combinations. It should be noted that the proposed model was designed to predict forest fire risk, not to detect forest fires themselves.