Surface roughness crucially affects the hydrological and erosive behaviours of soils. In agricultural areas surface roughness is directly related to tillage, whose action strongly affects the key physical properties of soils and determines the occurrence and fate of several processes (e.g., surface storage, infiltration, etc.). The characterisation of surface roughness as a result of tillage operations is not straightforward, and numerous parameters and indices have been proposed for quantifying it. In this article, a database of 164 profiles (each 5 m long), measured in 5 different roughness classes, was analysed. Four roughness classes corresponded to typical tillage operations (i.e., mouldboard, harrow, seedbed, etc.), and the fifth represented a seedbed soil that was subject to rainfall. The aim of the research was to evaluate and select the surface roughness parameters that best characterised and quantified the surface roughness caused by typical tillage operations. In total, 21 roughness parameters (divided into 4 categories) were assessed. The parameters that best separated and characterised the different roughness classes were the limiting elevation difference (LD) and the Mean Upslope Depression index (MUD); however, the parameters most sensitive to rainfall action on seedbed soils were limiting slope (LS) and the crossover lengths measured with the semivariogram method (lSMV) and

Soil surface roughness strongly affects the scattering 1 of microwaves on the soil surface and determines the backscat-2 tering coefficient (σ 0) observed by radar sensors. Previous 3 studies have shown important scale issues that compromise the 4 measurement and parameterization of roughness especially in 5 agricultural soils. The objective of this paper was to determine 6 the roughness scales involved in the backscattering process over 7 agricultural soils. With this aim, a database of 132 5-m profiles 8 taken on agricultural soils with different tillage conditions was 9 used. These measurements were acquired coinciding with a 10 series of ENVISAT/ASAR observations. Roughness profiles were 11 processed considering three different scaling issues: 1) influence 12 of measurement range; 2) influence of low-frequency rough-13 ness components; and 3) influence of high-frequency roughness 14 components. For each of these issues, eight different roughness 15 parameters were computed and the following aspects were eval-16 uated: 1) roughness parameters values; 2) correlation with σ 0 ; 17 and 3) goodness-of-fit of the Oh model. Most parameters had a 18 significant correlation with σ 0 especially the fractal dimension, 19 the peak frequency, and the initial slope of the autocorrela-20 tion function. These parameters had higher correlations than 21 classical parameters such as the standard deviation of surface 22 heights or the correlation length. Very small differences were 23 observed when longer than 1-m profiles were used as well as when 24 small-scale roughness components (<5 cm) or large-scale rough-25 ness components (>100 cm) were disregarded. In conclusion, 26 the medium-frequency roughness components (scale of 5-100 cm) 27 seem to be the most influential scales in the radar backscattering 28 process on agricultural soils.

The surface roughness of agricultural soils is mainly related to the type of tillage performed, typically consisting of oriented and random components. Traditionally, soil surface roughness (SSR) characterization has been difficult due to its high spatial variability and the sensitivity of roughness parameters to the characteristics of the instruments, including its measurement scale. Recent advances in surveying have greatly improved the spatial resolution, extent, and availability of surface elevation datasets. However, it is still unknown how new roughness measurements relates with the conventional roughness measurements such as 2D profiles acquired by laser profilometers. The objective of this study was to evaluate the suitability of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) photogrammetry techniques for quantifying SSR over different agricultural soils. With this aim, an experiment was carried out in three plots (5 × 5 m) representing different roughness conditions, where TLS and SfM photogrammetry measurements were co‐registered with 2D profiles obtained using a laser profilometer. Differences between new and conventional roughness measurement techniques were evaluated visually and quantitatively using regression analysis and comparing the values of six different roughness parameters. TLS and SfM photogrammetry measurements were further compared by evaluating multi‐directional roughness parameters and analyzing corresponding Digital Elevation Models. The results obtained demonstrate the ability of both TLS and SfM photogrammetry techniques to measure 3D SSR over agricultural soils. However, profiles obtained with both techniques (especially SfM photogrammetry) showed a loss of high‐frequency elevation information that affected the values of some parameters (e.g. initial slope of the autocorrelation function, peak frequency and tortuosity). Nevertheless, both TLS and SfM photogrammetry provide a massive amount of 3D information that enables a detailed analysis of surface roughness, which is relevant for multiple applications, such as those focused in hydrological and soil erosion processes and microwave scattering. © 2019 John Wiley & Sons, Ltd.

SAR (Synthetic Aperture Radar) sensors measure the backscatter ( 0 ) of land covers and SAR images have a number of applications in agricultural soils (soil moisture, crop monitoring, etc.) but the surface roughness of these soils complicates their interpretation and determination of quantitative estimates of useful parameters. The aim of this study is to quantify the spatial variability of different roughness parameters and the sensitivity of  0 to them measured at different scales. Ten Envisat/ASAR images acquired between September 2004 and January 2005 on an agricultural area with 10 control plots are analyzed. 132 roughness profiles of 5 m length were measured, and 21 different parameters were calculated. The results show considerable differences in the spatial variability of the parameters and differed depending on the type of parameter in the correlation analysis. This study can be useful to identify roughness parameters and scales that maximize their sensitivity to C-band backscatter.

Soil surface roughness determines the backscatter 1 coefficient observed by radar sensors. The objective of this letter 2 was to determine the surface roughness sample size required 3 in synthetic aperture radar applications and to provide some 4 guidelines on roughness characterization in agricultural soils 5 for these applications. With this aim, a data set consisting of 6 ten ENVISAT/ASAR observations acquired coinciding with soil 7 moisture and surface roughness surveys has been processed. The 8 analysis consisted of: 1) assessing the accuracies of roughness 9 parameters s and l depending on the number of 1-m-long profiles 10 measured per field; 2) computing the correlation of field aver-11 age roughness parameters with backscatter observations; and 12 3) evaluating the goodness of fit of three widely used backscatter 13 models, i.e., integral equation model (IEM), geometrical optics 14 model (GOM), and Oh model. The results obtained illustrate a 15 different behavior of the two roughness parameters. A minimum 16 of 10-15 profiles can be considered sufficient for an accurate 17 determination of s, while 20 profiles might still be not enough for 18 accurately estimating l. The correlation analysis revealed a clear 19 sensitivity of backscatter to surface roughness. For sample sizes 20 >15 profiles, R values were as high as 0.6 for s and ∼0.35 for l, 21 while for smaller sample sizes R values dropped significantly. 22 Similar results were obtained when applying the backscatter 23 models, with enhanced model precision for larger sample sizes. 24 However, IEM and GOM results were poorer than those obtained 25 with the Oh model and more affected by lower sample sizes, 26 probably due to larger uncertainly of l.

Abstract:Depression storage (DS) is the maximum storage of precipitation and runoff in the soil surface at a given slope. The DS is determined by soil roughness that in agricultural soils is largely affected by tillage. The direct measurement of DS is not straightforward because of the natural permeability of the soil. Therefore, DS has generally been estimated from 2D/3D empirical relationships and numerical algorithms based on roughness indexes and height measurements of the soil surface, respectively. The objective of this work was to evaluate the performance of some 2D models for DS, using direct and reliable measurements of DS in an agricultural soil as reference values. The study was carried out in experimental microplots where DS was measured in six situations resulting from the combination of three types of tillage carried out parallel and perpendicular to the main slope. Those data were used as reference to evaluate four empirical models and a numerical method. Longitudinal altitudinal profiles of the relief were obtained by a laser profilometer. Infiltration measurements were carried out before and after tillage. The DS was largely affected by tillage and its direction. Highest values of DS are found on rougher surfaces mainly when macroforms cut off the dominant slope. The empirical models had a limited performance while the numerical method was the most effective, even so, with an important variability. In addition, a correct hydrological management should take into account that each type of soil tillage affects infiltration rate differently.

Soil surface roughness strongly affects the scattering of microwaves and determines the backscattering coefficient observed by SAR (Synthetic Aperture Radar) sensors. The aim of this study is to analyze the influence of the spatial resolution of Terrestrial Laser Scanner (TLS) and Structure from Motion (SfM) techniques to parameterize surface roughness over agricultural soils. Three experimental plots (5 x 5 meters) representing different roughness conditions were measured by TLS and SfM techniques. Roughness parameters (s and l) were calculated from profiles obtained at different spatial resolutions in parallel and in perpendicular to the tillage direction on each plot. The results showed minor differences in the parameters values between both techniques and, in general, a decreasing trend and an increasing trend for lower spatial resolutions for parameter s and l, respectively.

Airborne LiDAR sensors capture three-dimensional information of the Earth, useful for obtaining high accuracy Digital Terrain Models (DTM). The Spanish National Plan for Aerial Orthophotography (PNOA) is an initiative of the Spanish Geographical Institute whereby nationwide LiDAR datasets are periodically acquired and made available to the public as .las files and value added products (e.g., DTM). The objective of this study is to assess the added value of PNOA LiDAR DTMs by comparing them to DTMs obtained through classical photogrammetric techniques. With this aim, four areas of interest were selected in Navarre (north of Spain), in areas with challenging characteristics such as forests, karst landforms, agricultural terraces and ravines. A 5x5 m DTM obtained with classical photogrammetry in 2008 was compared with a LiDAR DTM of the same pixel size obtained in 2011, assuming no significant changes occurred in this time. Height differences were evaluated, as well as slope, aspect and curvature differences. Besides, a multiresolution analysis was carried out to quantify how DTM smoothing affected height variations between neighbor pixels, measured with the standard deviation on a 5x5 window. The results obtained showed that the LiDAR DTMs provided an enhanced description of topography, particularly under forests and in areas with complex topography.