Effects of Land Surface Temperature vs. Air Temperature in Estimating Live Fuel Moisture Content

Live fuel moisture content (LFMC) is a key variable for affecting the occurrence of wildfires and a critical basis for constructing fire risk prediction systems. Meteorological indicators, commonly used in LFMC estimation, are often derived from reanalysis datasets of meteorological data. However, since air temperature is highly correlated with land surface temperature (LST), utilizing LST extracted from thermal infrared remote sensing data holds the potential to substitute air temperature in estimating LFMC and enhance accuracy. In this study, we estimated LFMC by involving both daytime and nighttime LST as well as air temperature from meteorological products, considering the lag effect of climate on LFMC. Our results indicate that model using nighttime LST yields the most accurate inversion results, followed by that utilizing daytime LST. Model using air temperature exhibits the lowest accuracy. However, employing all three variables concurrently can achieve higher precision in LFMC estimation.