"Estimating soil surface roughness by proximal sensing for soil erosion modeling implementation at field scale".

Area of Science:

• Soil Science
• Agricultural Engineering
• Remote Sensing

Background:

• Soil surface roughness (SSR) significantly impacts hydrological processes and soil erosion.
• Accurate SSR measurements are crucial for soil erosion modeling.
• Traditional SSR measurement methods are often time-consuming and inaccurate.

Purpose of the Study:

• To introduce and validate a novel RGB-Depth camera technique for measuring SSR and canopy cover (CC).
• To assess the impact of spatially explicit SSR data on soil erosion model performance.
• To evaluate the utility of camera-derived data for soil erosion factor calculations.

Main Methods:

• Utilized an RGB-Depth camera to generate high-resolution Digital Elevation Models (DEMs) for SSR quantification.
• Measured canopy cover (CC) from camera images.
• Integrated derived SSR and CC indices into the Morgan-Morgan-Finney (MMF) model for sediment yield prediction.
• Compared model predictions with long-term soil erosion data in Italian vineyards.
• Applied derived values for the C-factor in the RUSLE model.

Main Results:

• The MMF model showed satisfactory to good performance in predicting soil loss, especially when using spatialized SSR data.
• Spatialized SSR data improved model performance compared to uniform SSR values for both tilled and permanent ground cover plots.
• The camera-derived measurements proved useful for obtaining RUSLE C-factors, offering an alternative to tabular values.

Conclusions:

• The RGB-Depth camera technique provides a reliable and efficient method for measuring SSR and CC.
• Spatially explicit soil roughness data significantly enhances the accuracy of soil erosion models.
• This novel approach offers practical applications for soil erosion assessment and management in agricultural settings.