Estimating Soil Surface Roughness With Models Based on the Information About Tillage Practises and Soil Parameters

The quantitative description of the soil surface roughness is necessary for effective monitoring of wind, water and tillage erosion, hydrological processes or greenhouse gas emissions. The aim of this work was to build soil roughness predictive models based on the type of tillage tool, the roughness indices and soil properties. The roughness formed by five tillage tools was determined. Two surface roughness indices: Height Standard Deviation (HSD) and T-3D (Tortuosity index) were calculated from Digital Elevation Model. The both roughness indices demonstrated a significant correlation, however, they provided different information about soil roughness. The HSD describes roughness on the macro, while T-3D refers to the micro scale. Hence, our findings show that a single index is not sufficient to describe the roughness of post-treatment surface. The linear and random forest models were built to describe the relationships between the roughness indices, type of tillage tool and soil properties. The HSD analysis indicated that the type of tillage tools had the greatest impact on post-treatment roughness. In contrast, T-3D analysis found soil texture to have a significant effect, together with tillage tools. In all modeling scenarios, T-3D was more accurately predicted than HSD by both the linear (R-2 = 0.62 vs. R-2 = 0.60) and random forest models (R-2 = 0.58 vs. R-2 = 0.55). Predictive soil surface roughness models can be applied effectively for estimating water retention in soil, the intensity and speed of surface water flow, soil erosion, the level of reflected shortwave solar radiation or soil properties by remote sensing techniques.

Automated summary

The aim of this study was to build predictive models for soil roughness based on different tillage tools, roughness indices, and soil properties. The results showed that a single index is not sufficient to accurately describe post-treatment soil roughness. Linear and random forest models were built to analyze the relationships between roughness indices, tillage tools, and soil properties. The models demonstrated the importance of considering both the macro and micro scale roughness indices. These predictive models can be effectively applied to estimate various soil properties.