Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling

Fuel moisture content (FMC) is an important parameter in forest fire modeling. We investigated the performance of genetic algorithms with partial least squares (GA-PLS) modeling to retrieve live FMC and its components, equivalent water thickness (EWT) and dry matter content (DM), from fresh leaf reflectance in the leaf optical properties experiment dataset. The results show that GA-PLS achieved a good estimation of FMC directly (R-2 = 0.878-0.893) or indirectly (R-2 = 0.815-0.862) through the joint retrieval of EWT and DM; future work is required to assess the effectiveness of GA-PLS when applied to datasets that consist of low FMC values.