High-Resolution prediction of soil pH in European temperate forests using Sentinel-2 and ancillary environmental data

Soil pH is a key indicator for understanding soil health status in forested ecosystems, yet high-resolution mapping of this variable, especially at a 30-m spatial resolution, remains limited. This study uses Sentinel-2 spectral data, in-situ soil pH measurements, topsoil physical properties from the Land Use/Cover Area Frame Survey (LUCAS) database, and elevation data to estimate soil pH across temperate forests in Europe using a Random Forest model. Despite challenges in signal penetration due to forest canopy cover, the model achieved high prediction accuracy (R² = 0.62) at 30 m resolution. Bulk density, available water capacity, and clay content were the most influential physical predictors, while Sentinel-2 bands, particularly SWIR (1.610 and 2.190 μm), NIR (0.842 μm), and red-edge (0.705 and 0.783 μm), captured key vegetation responses related to soil acidity. Spatial analysis showed higher model accuracy in central and southern Europe, with reduced performance in Scandinavia, likely due to more acidic soils and extreme seasonal variation. The model also revealed significant pH differences among forest types, with deciduous forests showing the highest values and coniferous the lowest. These findings demonstrate the potential of high-resolution remote sensing data for monitoring soil pH, supporting forest management, biodiversity conservation, and climate adaptation strategies

Comparing Forest Species Emissivity Using Airborne Thermal Infrared Hyperspectral data in a Mixed Temperate Forest

The need to identify and remotely speciate different vegetation classes in mixed forest environments continues to be an important area for ecosystem conservation and management purposes. Such applications generally rely on the biochemical and biophysical properties found in the VNIR (0.3–1.0 μm) and SWIR (1.0–2.5 μm) regions. Nevertheless, foliar spectral behaviour in the TIR (8–14 μm) domain hassignificant interspecies variability that has been shown to correlate with the spectral features of key plant constituents. Different plant species have been successfully discriminated in the laboratory using leaf emissivity spectra. However, given the complexity of emissivity at the canopy level, species discrimination using canopy emissivity spectra obtained from airborne TIR remains unexplored. This study aims to compare the differences in the canopy emissivity spectra obtained from the airborne TIR hyperspectral data among and between various vegetation covers in a mixed temperate forest