Estimation of Soil Surface Roughness Parameters Under Simulated Rainfall Using Spectral Reflectance in Optical Domain

The purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS-NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T-3D (Tortuosity index). Although both shared a significant correlation with SSR, the T-3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T-3D was obtained with the raw spectra (RAW) (R-2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R-2 = 0.71) or the Savitzky-Golay (SG) method (R-2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs - Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725-820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450-772 nm) were most important in models based on transformed spectra.

Automated summary

The purpose of this study was to parameterize soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS-NIR range, which is important for monitoring the state of soil surfaces. The study found that there is a relationship between SSR parameters and soil spectra, and different wavelengths are best predictors depending on the spectral transformation method.