Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

Dead fuel moisture content (DFMC) is of important significance for estimating and predicting forest wildfire risk, in which 1-h dead fuel is most critical as the easiest fuel to ignite. Current methodologies based on empirical and physical models rely heavily on meteorological data from uneven and sparse stations. In contrast, satellite data become a better choice since its continuous surface observations. Of all satellites, Himawari-8 is the most appropriate data to meet the rapid changes of DFMC throughout the day owing to its high time resolution. Thus, this study explored the application of 1-h DFMC predicting using machine learning from Himawari -8 data. Random forest was selected for the prediction and linear regression was used for comparison. The results showed that random forest has a satisfactory performance with higher R2 (0.53) and lower RMSE (3.15%) than that of linear regression (R2=0.21, RMSE=5.47%). The research suggested that predicting 1-h DFMC at regional scales using machine learning from Himawari -8 data is promising.