This paper quantitatively explores, in terms of roughness indices, the effect of soil surface irregularities on the diurnal variation of the broadband blue-sky albedo of a large range of soil properties. Field studies were carried out on cultivated and uncultivated soil surfaces in Poland and Israel that vary in roughness and brightness. It was found that these irregularities, formed by different agricultural equipment and modified by rain or sprinkler irrigation, can be quantified by two roughness indices. Soil roughness not only affects the overall level of the diurnal variation of the albedo, but also affects the intensity of the diurnal increase from the solar zenith angle (θ _s ) at the local noon to about 75°- 80°. The roughness indices are variables that precisely determine only the albedo at the local solar noon of soils with the same color value. If the contents of soil organic carbon (SOC) and calcium carbonate are treated as the dominant variables, combined with one of the indices, these three variables together would significantly describe the albedo at the local solar noon of all soil surfaces. The soils, with their high irregularities, showed almost no rising values of albedo at a θ _s lower than 75°, while the smooth soil surfaces exhibited a gradual increase of the albedo at these angles. It is concluded that the roughness indices provide sufficient means to accurately describe the diurnal variation of the albedo of a wide range of surfaces, disregarding other soil properties.

Various remote sensing methods, including diffuse and contact reflectance spectroscopy (DRS and CRS, respectively) as well as proximal soil sensing (PSS), have been widely used for obtaining information about the soil properties. The influence of soil surface roughness on soil spectra obtained with a FieldSpecPro (ASD) spectroradiometer using three measurement techniques was investigated. The PSS spectra in outdoor (PSS-O) and laboratory (PSS-L) conditions were collected and then compared with the spectra of the same soil recorded using Muglight (ML) adapter. PSS measurements of each soil sample were taken at three surface roughness levels: the lowest roughness level, representing the rolled surface, the second representing a moderately rough (MR) surface of medium-sized clods and aggregates, and the third level with the highest soil surface roughness, where the clods and aggregates, with size beyond their assessed mean size for a given soil. The roughness indices height standard deviation (HSD) and tortuosity (T _3D ) were closely related to the size of the aggregates and clods forming the soil surface. The ML spectra of ground samples were more strongly correlated with the PSS-L spectra than with the PSS-O spectra, and mean coefficient of determination (R ² ) for the spectra obtained at three levels of roughness was 0.99 and 0.95, respectively. With the increase of surface roughness, the differences between the level of ML and both PSS-L and PSS-O spectra increased. The relative difference (RD) calculated for PSS-L and PSS-O spectra in relation to ML spectra was better explained by the T _3D (R ² = 0.82), than the HSD (R ² = 0.68).

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 (R² = 0.62 vs. R² = 0.60) and random forest models (R² = 0.58 vs. R² = 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.