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

Abstract

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.