Published March 2, 2023 | Version 1.0.0
Journal article Open

Global live fuel moisture content (LFMC) dataset V1.0.0

Creators

  • 1. University of Electronic Science and Technology of China
  • 1. University of Electronic Science and Technology of China
  • 2. The Australian National University
  • 3. University of California Davis

Description

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This dataset is the first FMC product on a global scale from 2000 to 2018. This global FMC product was generated based on a physically-based methodology under the radiative transfer process using reflectance data from MODIS (Moderate Resolution Imaging Spectrometer) 's MCD43A4 Collection 6. The global FMC product was validated using worldwide FMC measurements from 120 sites with the statistically significant agreement between retrieved and measured FMC (R2 = 0.62, RMSE = 34.57%, p < 0.01). It is anticipated that this global FMC product can assist in wildfire danger early prediction, suppression, and response, as well as improve awareness of fire risk to life and property.

For details: https://www.sciencedirect.com/science/article/pii/S0303243421000611

Note: The original LFMC dataset has a spatial resolution of 0.005 degrees (~500 meters). However, due to its large size, it could not be hosted on Zenodo. Therefore, we resampled it to a resolution of 0.05 degrees (~5000 meters) here.

Notes

https://www.sciencedirect.com/science/article/pii/S0303243421000611

Files

FMC_0.05degree_8day_Global-2000.zip

Files (4.7 GB)

Name Size Download all
md5:9a6c7b86040428cc9e16f12f9991bdc6
200.3 MB Preview Download
md5:06d3f5100c57f5745310794dea70633a
194.2 MB Preview Download
md5:406ca91e85a76c82bdd86e1619752b33
234.9 MB Preview Download
md5:2530ccee851ac9788c52e18dcf59d58b
248.0 MB Preview Download
md5:a9f122585d7ecf42f8db9d1f2225948c
248.7 MB Preview Download
md5:17d006c967899d3a4fe9bdd459bc44b7
252.8 MB Preview Download
md5:99b25cb78dee80d7ee3d92f0a7d532c1
255.6 MB Preview Download
md5:e0f1f569c0fad594bce9fd9a9b4f58ab
254.8 MB Preview Download
md5:0b5139498267f037f110967850b60c16
253.5 MB Preview Download
md5:8c5a05919f55a94d61869643878e8ff7
249.5 MB Preview Download
md5:5a1e2fab013b8a9b2ce51d45a4285ad8
257.5 MB Preview Download
md5:c5eda93cc9ee268aa3073426b5e04e63
255.9 MB Preview Download
md5:7d7aaad7f4785c637542cd8b649c9427
251.1 MB Preview Download
md5:10dcb1e66e7182bdeec5230167ed3cc9
256.2 MB Preview Download
md5:de8d271dda8b99523b9c59c29266c89b
256.7 MB Preview Download
md5:c3bd17809bb645f1cd78417d06e7d321
257.2 MB Preview Download
md5:7c6d59db76403fc094625fa288cf1956
259.9 MB Preview Download
md5:20cc75102fbf0e1cd47e37e383f8cccc
257.2 MB Preview Download
md5:9172e2fb60361f910348ff858d83b594
256.9 MB Preview Download

Additional details

Related works

Is cited by
Journal article: 10.1016/j.jag.2021.102354 (DOI)

References

  • Xingwen, Quan, Marta Yebra*, David Riaño, Binbin He*, Gengke Lai, Xiangzhuo Liu. Global fuel moisture content mapping from MODIS[J], International Journal of Applied Earth Observation and Geoinformation,2021,101:102354.