The Potential of Sentinel-1 Data for Coniferous Forest Fuel Loads Estimation in Southwest of Sichuan, China

Forest fuel load plays an important role in fire ignition, spread and intensity. Accurate spatial distribution information of fuel load is vital for fire managers to make decisions. However, most of the existing studies using spectral or texture information of optical data, which is seriously affected by atmospheric conditions with limited penetration. SAR with all-day and all-weather work characteristics provides a favorable opportunity to estimate fuel loads in Southwest China with cloudy and foggy. Moreover, SAR can penetrate through leaves to branches and stems, which reflects the forest vertical structure well. However, there was little research applying SAR data to fuel load estimation. In this study, we focus on forest above ground live fuel load estimation (biomass fractions), including stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We explored the potential of dual polarimetric data, Sentinel-1 for coniferous forest fuel loads estimation in the Southwest of Sichuan, China. To understand scattering mechanisms at C-band in Pinus yunnanensis forest, the Michigan Microwave Canopy Scattering (MIMICS) radiative transfer model was used. And Multiple Linear Regression (MLR) method was used to estimate fuel loads. Results show that VH and VV polarizations were both sensitive to three types of fuel load. Combined with the simulation of MIMICS, we found VH polarization was more sensitive to FFL while VV was more sensitive to SFL. Additionally, Sentinel-1 performs well in all three types of fuel load estimation (FFL: R2 = 0.52, RMSE=1.43 Tons/ha; BFL: R2=0.58, RMSE=1.88 Tons/ha; SFL: R2=0.57, RMSE=2.97 Tons/ha), indicating that Sentinel-1 data has great potential in FFL estimation and fire prevention.