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

• Published in: Geoderma 2023-02, Vol.430, p.116290, Article 116290
• Main Authors: Beltrán-Marcos, David, Suárez-Seoane, Susana, Fernández-Guisuraga, José Manuel, Fernández-García, Víctor, Marcos, Elena, Calvo, Leonor
• Format: Article
• Language: English
• Subjects: Image fusion; Sentinel-2; Soil organic carbon; Soil properties; UAV; Wildfire
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2022.116290

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