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Accurate estimation of fuel moisture content (FMC) is essential for determining the physiological status of vegetation and assessing fire risk. As FMC is not directly or physically linked to light absorption processes, its accurate estimation from hyperspectral data remains a challenge. Inspired by the recent boosting of applying fractional order derivative (FOD) spectra–based indices into leaf mass per area estimation, we investigated the potential of using FOD indices to retrieve FMC across a range of plant species of different vegetation types. We developed fractional indices to retrieve FMC based on a composite dataset consisting of 2891 leaf samples from different species. The evaluation results showed that the newly identified ND(1900, 2095) index based on the 0.8-order fractional derivative spectra had a high prospect, with an overall R 2  value of 0.90, to capture a wide range (42–1321%) of FMCs in different plant species. Although the R 2  values dropped when applied to each specific vegetation type, the regressions remained highly significant. Our results demonstrated the great potential of FOD-derived indices to estimate a wide range of live FMCs from hyperspectral reflected information.

Exploring low order fractional derivative spectra indices for estimating leaf fuel moisture content across a variety of plant species