QUAN, Xingwen; YEBRA, Marta; RIAÑO, David; HE, Binbin; LAI, Gengke; LIU, Xiangzhuo

doi:10.1016/j.jag.2021.102354

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QUAN, Xingwen
University of Electronic Science and Technology of China [Chengdu] [UESTC]

YEBRA, Marta
Fenner School of Environment and Society

RIAÑO, David
Center for Spatial Technologies and Remote Sensing [CSTARS]

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International Journal of Applied Earth Observation and Geoinformation. 2021-05-19, vol. 101, p. 1-15

Elsevier

Résumé en anglais

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE = 32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property.< Réduire

Mots clés en anglais

Fire Danger

Fuel Moisture Content

Global Scale

Model Inversion

MODIS

Radiative Transfer Model

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Global fuel moisture content mapping from MODIS

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