Document


Title

Global fuel moisture content mapping from MODIS
Document Type: Journal Article
Author(s): Xingwen Quan; Marta Yebra; David Riaño; Binbin He; Gengke Lai; Xiangzhuo Liu
Publication Year: 2021

Cataloging Information

Keyword(s):
  • fuel moisture content
  • global scale
  • live fuels
  • model inversions
  • MODIS - Moderate Resolution Imaging Spectroradiometer
  • radiative transfer models
Record Maintained By:
Record Last Modified: June 9, 2021
FRAMES Record Number: 63782

Description

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.

Online Link(s):
Citation:
Quan, Xingwen; Yebra, Marta; Riaño, David; He, Binbin; Lai, Gengke; Liu, Xiangzhuo. 2021. Global fuel moisture content mapping from MODIS. International Journal of Applied Earth Observation and Geoinformation 101:102354.