A Physical Method for Crown Foliage Fuel Load Retrieval from Landsat Data: Toward Crown Fire Danger Assessment

This study presents a radiative transfer process and machine learning combined method to estimate the crown foliage fuel load (FFL), an important factor influencing the characteristics of crown fires. To this end, the GeoSail, SAIL and PROSPECT radiative transfer models (RTMs) were firstly coupled together to simulate the near realistic scenario of a two-layered forest structure. For the backward inversion process, the coupled RTMs were linked to the machine learning models of multi-layer perceptron (MLP) to train this model and then derive the FFL estimates from the Landsat products. The performance of retrieved FFL was validated and compared with the traditional look-up table (LUT) method. Results showed that the MLP performed better than LUT, revealing the reasonable skill of the combination of RTMs and machine learning modeling in deriving the FFL from remote sensing data and putting the insight into wildfire danger assessment from space.