Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery burned forest ecosystems. Here, we aimed to assess potential combining multispectral imagery different spectral and resolutions estimate indicators burn severity. study was conducted in a area located northwest Iberian Peninsula (Spain). One month fire, measured severity field using an adapted protocol Composite Burn Index (CBI). Then, performed sampling analyze three properties potentially indicatives fire-induced changes: mean weight diameter (MWD), moisture content (SMC) organic carbon (SOC). Additionally, collected post-fire from Sentinel-2A MSI satellite sensor (10–20 m resolution), as well Parrot Sequoia camera board unmanned aerial vehicle (UAV; 0.50 resolution). A Gram-Schmidt (GS) image sharpening technique used increase resolution Sentinel-2 bands fuse these data with UAV information. performance parameters determined trough machine learning decision tree, relationship between reflectance values (UAV, fused UAV-Sentinel-2 images) analyzed by means support vector (SVM) regression models. All considered decreased their value severity, but content, and, lesser extent, discriminated best among classes (accuracy = 91.18 %; Kappa 0.82). derived monitor effects wildfire characteristics outstanding, particularly for case (R2 0.52; RPD 1.47). This highlights advantages images produce spatially spectrally enhanced images, which be relevant estimating main impacts areas where emergency actions need applied.