Evaluating the Sentinel-2a Satellite Data for Fuel Moisture Content Retrieval

Fuel moisture content (FMC) of vegetation canopy is a critical variable in affecting wildfire behavior. Methodologies based on multiple sources of remote sensing data have shown a prominent advantage for spatial and temporal FMC mapping. However, there is no study focused on FMC retrieval using the Sentinel-2A satellite data to date. This study is to evaluate the performance of this data for FMC retrieval under the framework of the multiple coupled radiative transfer models. Due to the limited field measurements and discontinuous satellite data, only 15 field measurements from USA, South Africa, Australia and France were available for the validation of the retrieved FMC. Results show that the retrieved FMCs were promising with R2 = 0.64 and RMSE = 47.16%, which demonstrated the potential usage of the Sentinel-2A data for FMC mapping and further applications for early-warning of wildfire risk.

[1]  K. Huemmrich.  The GeoSail model: a simple addition to the SAIL model to describe discontinuous canopy reflectance , 2001 .

[2]  Xing Li,et al.  Retrieval of forest fuel moisture content using a coupled radiative transfer model , 2017, Environ. Model. Softw..

[3]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[4]  Zhi Tang,et al.  Estimation of Grassland Live Fuel Moisture Content From Ratio of Canopy Water Content and Foliage Dry Biomass , 2015, IEEE Geoscience and Remote Sensing Letters.

[5]  Fred A. Kruse,et al.  The Spectral Image Processing System (SIPS) - Interactive visualization and analysis of imaging spectrometer data , 1993 .

[6]  D. Riaño,et al.  Estimation of live fuel moisture content from MODIS images for fire risk assessment , 2008 .

[7]  E. Chuvieco,et al.  Conversion of fuel moisture content values to ignition potential for integrated fire danger assessment , 2004 .

[8]  Matthias M. Boer,et al.  Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data. , 2016 .

[9]  Laurie A. Chisholm,et al.  Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data , 2012 .

[10]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[11]  F. M. Danson,et al.  A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving t , 2013 .

[12]  Pierre Grussenmeyer,et al.  Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery , 2018, Remote Sensing of Environment.

[13]  A. Skidmore,et al.  Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland , 2008 .

[14]  D. Riaño,et al.  Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment , 2002 .

[15]  David Riaño,et al.  A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing , 2018, Remote Sensing of Environment.

[16]  D. Riaño,et al.  Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating , 2004 .

[17]  Emilio Chuvieco,et al.  Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem , 2009 .