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Article

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

1
Joint Research Unit CTFC-AGROTECNIO-CERCA Center, 25198 Lleida, Spain
2
Department of Crop and Forest Sciences, University of Lleida, 25198 Lleida, Spain
3
Department of Forest Engineering, Grupo ERSAF, DendroDat Lab, Campus de Rabanales, Universidad de Córdoba, Crta. IV, km. 396, 14071 Córdoba, Spain
4
School of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
*
Author to whom correspondence should be addressed.
Academic Editor: Víctor Fernández-García
Remote Sens. 2022, 14(13), 3162; https://doi.org/10.3390/rs14133162
Received: 25 May 2022 / Revised: 27 June 2022 / Accepted: 29 June 2022 / Published: 1 July 2022
Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMCRF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMCRF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin. View Full-Text
Keywords: live fuel moisture content; wildfire; MODIS; spectral indices; land surface temperature; random forests live fuel moisture content; wildfire; MODIS; spectral indices; land surface temperature; random forests
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MDPI and ACS Style

Cunill Camprubí, À.; González-Moreno, P.; Resco de Dios, V. Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data. Remote Sens. 2022, 14, 3162. https://doi.org/10.3390/rs14133162

AMA Style

Cunill Camprubí À, González-Moreno P, Resco de Dios V. Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data. Remote Sensing. 2022; 14(13):3162. https://doi.org/10.3390/rs14133162

Chicago/Turabian Style

Cunill Camprubí, Àngel, Pablo González-Moreno, and Víctor Resco de Dios. 2022. "Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data" Remote Sensing 14, no. 13: 3162. https://doi.org/10.3390/rs14133162

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