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

Area of Science:

• Ecology
• Soil Science
• Remote Sensing

Background:

• Soil pH is crucial for forest health and ecosystem function.
• High-resolution soil pH mapping in forests is currently limited.
• Understanding soil pH variability is essential for effective forest management.

Purpose of the Study:

• To develop a high-resolution (30m) soil pH mapping model for European temperate forests.
• To assess the effectiveness of Sentinel-2 spectral data and physical soil properties in predicting soil pH.
• To identify key predictors influencing soil pH in forested ecosystems.

Main Methods:

• Utilized Sentinel-2 spectral data, in-situ soil pH measurements, and the LUCAS database for topsoil physical properties.
• Employed a Random Forest model to estimate soil pH across European temperate forests.
• Incorporated elevation data as an additional predictor variable.

Main Results:

• Achieved high prediction accuracy (R² = 0.62) for soil pH at 30m resolution, despite forest canopy challenges.
• Identified bulk density, available water capacity, and clay content as key physical predictors.
• Sentinel-2 SWIR, NIR, and red-edge bands proved valuable in capturing vegetation responses linked to soil acidity.
• Model accuracy varied spatially, with higher performance in central/southern Europe and lower performance in Scandinavia.
• Significant pH differences were observed among forest types, with deciduous forests being less acidic than coniferous forests.

Conclusions:

• High-resolution remote sensing, particularly Sentinel-2, shows significant potential for monitoring forest soil pH.
• The developed model provides a valuable tool for supporting sustainable forest management, biodiversity conservation, and climate adaptation strategies.
• Further research could refine models for regions with challenging conditions like Scandinavia.