Effects Of Soil Surface Roughness Research Articles

Soil surface roughness impacts the risk arising from a hypothetical urban radiological dispersive device activation.

This study considers a deliberate hypothetical release of radioactive material over an inhabited urban zone. The event is initiated by the activation of a radiological dispersion device. The main threat is the deposition of radioactive material onto the soil's surface. The radiation represents the threat-defining risks, which depend on the main variables, i.e. soil surface roughness, sex, age of the exposed individuals and the moment of the release (day or nighttime). This study aims to evaluate the effect of soil surface roughness on the radiological risk. The simulation was performed by an analytical method using the HotSpot Health Physics code within the first 100h. The results found relevant elements that allow for differentiating consequences as a function of the time of release (whether daytime or nighttime), thus allowing decision-makers to be supported with a little more detail about the situation, although in a critical initial phase.

Improving RUSLE predictions through UAV-based soil cover management factor (C) assessments: A novel approach for enhanced erosion analysis in sugarcane fields

The Universal Soil Loss Equation (USLE) and its derivatives express the combined effects of crop cover and rainfall patterns by the cover and management factor (C). Thus, the C-factor links the combined effect of soil surface roughness, vegetation, biomass cover, and rainfall patterns on soil erosion. This evaluation should be at each phenological stage. Due to the significant time and effort needed to access this factor for a crop, simplified methods are often used, disregarding the expected intra-annual variability and consequently increasing the uncertainty for soil loss modeling. In this scenario, we proposed a framework to collect input data at a fine-scale to estimate the C-factor by the original approach. For this, we collected data with a low-cost UAV at the middle of each phenological stage of sugarcane: sprouting, tillering, elongation, and maturation. We used orthomosaics, three vegetation indices (ExRmG, MGRVI, ViGREEN), digital surface models (DSM), and digital terrain model (DTM) to determine the canopy cover (CC), surface cover (SC), and soil roughness (SR), accessing the soil loss ratio (SLR) per phenological stage. Late on, we estimate the C-factor weighting the SLR by the rainfall erosivity. Our annual C-factor aligns with the most values applied to sugarcane studies and ranged from 0.0241 to 0.2938. Our results pointed out that using the proposed methods can access suitable annual C-factor for sugarcane areas. Furthermore, we highlighted the ViGREEN because it presented a significant performance in orthomosaics classification and has a potential already reported in other studies on C-factor at different scales.

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning

Measurements of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18 through June 13, 2005

Passive microwave signatures have been used to retrieve geophysical parameters, such as soil temperature [Njoku and Li, 1999], moisture [Jackson et al., 1995], and surface roughness [Wegmüller and Mätzler, 1999]. One of the challenges in the parameter retrieval is the effect of soil surface roughness on the microwave emission. We conducted soil surface roughness measurements as part of our fourth Microwave Water and Energy Balance Experiment (MicroWEX-4) to understand the effects of surface roughness on microwave signatures at 6.7 GHz (λ = 4.48 cm). The dataset will also be used to develop and validate surface roughness models. In this report, we summarize briefly the theoretical background of surface roughness characteristics and discuss methodology and results of the roughness experiments. This document is Circular 1483, one of a series of the Department of Agricultural and Biological Engineering, UF/IFAS Extension. Original publication date November 2005. CIR1483/AE363: Measurements of Soil Surface Roughness During the Fourth Microwave Water and Energy Balance Experiment: April 18–June 13, 2005 (ufl.edu)

Coupling effects of erosion and surface roughness on colluvial deposits under continuous rainfall

The colluvial deposits, resulting from the collapsing gully erosion, provide a large amount of sediment for subsequent water erosion in southeastern China. Previous studies have shown the apparent effects of rainfall, runoff and slope gradient on erosion of colluvial deposits, but paid little attention to the effect of soil surface roughness. The objective of this study was to investigate the relationship between surface roughness and erosion processes under continuous rainfall conditions. For this purpose, we repeated an indoor rainfall experiment 20 times on a colluvial deposit slope of 30°. The rainfall intensity was 3.33 mm min−1 and the rainfall lasted 60 min. We measured the sediment yield for each rainfall experiment and the related hydrodynamic parameters, including the flow velocity, Froude number, Reynolds number, Runoff power and Darcy-Weisbach resistance coefficient. We found that the gradual enrichment of coarse particles and gravel on the surface of the colluvial deposits led to an exponential decrease in sediment yield. The water erosion consists of three processes: (1) The emergence of the falling-sill, followed by the formation and rapid expansion of the rill; (2) The frequent expansion of the rill; and (3) The random landslide of the rill wall. The flow velocity, Froude number (Fr), Reynolds number (Re) and runoff power (ω) all showed a power decrease in contrast to a power increase of Darcy-Weisbach resistance coefficient (f) due to the influence of the roughness of colluvial deposits. The roughness and flow velocity can be used as good indicators for characterizing the water erosion intensity of the colluvial deposits. The findings will facilitate further research into the effect of roughness on the water erosion of colluvial deposits.

Representative elementary area issue in soil spectral measurements

Soil spectral characteristics from laboratory and in situ measurements are an effective source of information about various soil properties. However, features of scanned surfaces are unified by the drying and grounding of soil samples solely for laboratory measurements. Spectra scanned in situ are perturbed to varying degrees due to spatial variation of the soil’s moisture and roughness that results for the most part from cultivation practices and intrinsic soil aggregation. Even intensive data pre-processing and advanced modeling techniques cannot fully eliminate these signal modifications even though field spectra can be interpreted if and when the representativeness criterion is reached. This work is aimed at examining the effects of soil surface roughness on the size of viewing areas that fulfill the representative elementary area (REA) criterion for a stable spectra acquisition. Surface horizon’s samples with natural soil aggregates and undisturbed clods from five different soils were prepared on trays into three different soil surface roughness levels. Spectral measurements were carried out under laboratory conditions while incrementally increasing sensor height above the reference level at nadir, orthogonal to the target surface, and illuminated along the one plane at three zenithal angles (θs). Roughness of surfaces was measured using a laser scanner and described in terms of height standard deviation (HSD) of the soil surface area within its delineated basic digital elevation model (DEM) unit. To express equally the size and roughness of the viewed area, a single qualifier DRR (diameter to roughness ratio) was proposed. This is the ratio of the diameter of field of view (DFOV) to the surface roughness index HSD, and can be interpreted as numbers of structural elements placed along DFOV, where higher values indicate higher representativeness of the viewed area. In general, a stable spectrum is scanned at DRR starting from 60, and this criterion should be complied with at a minimum during proximal spectral measurements of soils. Furthermore, DRR above 60 is the only minimum criterion. Results of the work suggest the stabilization of spectral measurement at DRR close to 60, as well as the elimination of noise error at DRR close to 200.

Effects of soil surface roughness on soil processes and remote sensing data interpretation and its measuring techniques - a review

Surface roughness is a very important physical feature of soil, affecting various soil processes and accuracy of remote sensing data interpretation. Thus, there is a need to describe it quantitatively. The main aim of the paper was to show needs and benefits of collecting quantitative information about soil surface roughness which is the most relevant parameter used as an index to predict water and wind erosion. Surface roughness can reduce soil erosion and soil losses even by up to 31%. Thereby, it increases the development of fauna and flora and improves the structure of soil and its biological quality. In the first section of the paper there are presented definitions of soil roughness proposed by different authors. The next section explains how various factors influence soil surface roughness. Then, the categorization of soil surface roughness discussed in literature is presented. The next part of the paper includes information about a role of soil roughness in agricultural, soil science and a hydrology research. Moreover, soil surface roughness plays an important role in a remote sensing of soils. The knowledge of quantitative soil surface roughness allows more accurate interpretation of the soil properties from remote sensing data, because this soil feature can decrease soil spectra even over 70% and makes their analysis difficult. In addition, deepening knowledge about soil roughness will allow more precise conclusions about the amount of reflected shortwave solar radiation indirectly shaping the Earth’s climate. In the final section, the techniques for measuring and indices for describing soil roughness are shown. However, the authors prefer a photogrammetry technique for collecting these data, because it is quick and easy to use, ensuring high resolution and accuracy of data (about 1 mm) and the image processing is currently simplifid as software to process is absolutely affordable.

Effect of soil surface roughness on infiltration water, ponding and runoff on tilled soils under rainfall simulation experiments

Agriculture has a large effect on the properties of the soil and with that on soil hydrology. The partitioning of rainfall into infiltration and runoff is relevant to understand runoff generation, infiltration and soil erosion. Tillage manages soil surface properties and generates soil surface roughness (SSR) that affects the partitioning of the rainfall. The objective of this study was to quantify the amount of rainwater that infiltrates, is temporarily stored in surface depressions and flows out of the surfaces during rainfall events. A set of tillage-induced rough surfaces with slope steepness of 10° and 15° was used under simulated rainfall, and a smooth surface served as a control. Rainfall intensities were 60 and 120 mm h−1, and two soil erosion periods, overland flow erosion period (OFEP) and rill flow erosion period (RFEP), were monitored for each rainfall intensity. The results showed that for OFEP, infiltration water was 58% and 76% of the total rainwater on the rough surfaces and was approximately 1.5 and 2 times greater than that on the smooth surfaces for the different rainfall intensities. The surface runoff was consistently small for the OFEP but significantly increased for the RFEP. For example, for the RFEP, the amount of surface runoff was up to 78.66% of the total rainwater on the rough surfaces under rainfall of 120 mm h−1 in intensity. The amount of rainwater stored in surface depressions was significantly less than infiltration water and surface runoff for all conditions. The mean transformation ratio of rainwater into surface depression storage, infiltration water and surface runoff in the OFEP and RFEP was 0.07:0.49:0.44 for the rough surfaces and 0.01:0.29:0.70 for the smooth surfaces. For the tilled surfaces, more than 50% of rainwater was be harvested through tillage technique during a rainfall event, whereas for the smooth surfaces, only 29% of rainwater. Our result will be useful when evaluating the impact of tillage on soil moisture content and even studying soil erosion in agriculture land.

Effects of soil surface roughness on interrill erosion processes and sediment particle size distribution

Soil surface roughness significantly impacts runoff and erosion under rainfall. Few previous studies on runoff generation focused on the effects of soil surface roughness on the sediment particle size distribution (PSD), which greatly affects interrill erosion and sedimentation processes. To address this issue, a rainfall-simulation experiment was conducted with treatments that included two different initial soil surface roughnesses and two rainfall intensities. Soil surface roughness was determined by using photogrammetric method. For each simulated event, runoff and sediment samples were collected at different experimental times. The effective (undispersed) PSD of each sediment sample and the ultimate (after dispersion) PSD were used to investigate the detachment and transport mechanisms involved in sediment movement. The results show that soil surface roughness significantly delayed runoff initiation, but had no significant effect on the steady runoff rate. However, a significant difference in the soil loss rate was observed between the smooth and rough soil surfaces. Sediments from smooth soil surfaces were more depleted in clay-size particles, but more enriched in sand-size particles than those from rough soil surfaces, suggesting that erosion was less selective on smooth than on rough soil surfaces. The ratio of different sizes of transported sediment to the soil matrix indicates that most of the clay was eroded in the form of aggregates, silt-size particles were transported mainly as primary particles, and sand-size particles were predominantly aggregates of finer particles. Soil surface roughness has a crucial effect on the sediment size distribution and erosion processes. Significant differences of the enrichment ratios for the effective PSD and the ultimate PSD were observed under the two soil surface roughness treatments. These findings demonstrate that we should consider each particle size separately rather than use only the total sediment discharge in assessing and modeling interrill erosion processes.

Global Sensitivity Analysis of the L-MEB Model for Retrieving Soil Moisture

A global sensitivity analysis utilizing the extended Fourier amplitude sensitivity test is used to determine the parameter sensitivity of the L-band microwave emission of the biosphere (L-MEB) model. The results are analyzed from two perspectives of calibration and inversion. First, the parameters of surface soil moisture, soil roughness factor, vegetation optical depth at nadir, and effective land surface temperature are the four most sensitive parameters in the L-MEB model, demonstrating their possibility to be retrieved in the multiparameter retrieval approaches. Then, the high total sensitivity index (TSI) values of surface soil temperature in the analyses emphasize the importance of high-precision land surface temperature data in the surface soil moisture retrievals, especially for rougher or more vegetated surface conditions. Finally, our analysis indicates that TSI values are high for the soil surface roughness and vegetation optical depth model parameters but low for the vegetation structure, single scattering albedo, and soil roughness coefficient model parameters at incidence angles near nadir. This suggests that calibration experiments performed at small incidence angles may be appropriate for some but not all of the model parameters, which characterize the effect of soil surface roughness and vegetation on the terrestrial brightness temperature. Consequently, new calibration procedures that account for the different relative sensitivities of these model parameters at larger incidence angles may need to be developed in the future.

Effects of Soil Surface Roughness on Soil Reflectance Measured in Laboratory and Outdoor Conditions

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 ( $\mathbf{T}_\mathbf{3D}$ ) 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 ( $\mathbf{R}^\mathbf{2}$ ) 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 $\mathbf{T}_\mathbf{3D}$ ( $\mathbf{R}^{\mathbf{2}} = \mathbf{0.82}$ ), than the HSD ( $\mathbf{R}^{\mathbf{2}} = \mathbf{0.68}$ ).

Accuracy Assessment of IWCM Soil Moisture Estimation Model in Different Frequency and Polarization Bands

Radar backscattering coefficient has high dependence to dielectric constant of soil and many efforts have been done in the past to estimate soil moisture using Synthetic Aperture Radar (SAR). Soil moisture estimation in vegetated areas has some limitations and difficulties due to the effects of vegetation cover and soil surface roughness on radar backscattering coefficient. One of the widely used soil moisture estimation models in vegetated areas is Water Cloud Model (WCM) which has been improved and known as Improved Water Cloud Model (IWCM) recently. One way of improving soil moisture estimation accuracy in vegetated areas is to use optimum frequency and polarization band so as to minimize the effects of soil surface roughness and vegetation cover on radar back scattering coefficient. In this research, the accuracies of IWCM in different frequencies and polarizations have been assessed. The results showed that the IWCM has its highest accuracy in L-band, HV polarization mode. Also, by using the IWCM, sensitivities of radar waves to moisture of 0–3, 3–6 and 0–6 cm soil depths have been studied. The results demonstrated that radar waves have more sensitivity to the moisture content of 0–3 cm soil depth.

Modeling and Mapping Soil Moisture of Plateau Pasture Using RADARSAT-2 Imagery

Accurate soil moisture retrieval of a large area in high resolution is significant for plateau pasture. The object of this paper is to investigate the estimation of volumetric soil moisture in vegetated areas of plateau pasture using fully polarimetric C-band RADARSAT-2 SAR (Synthetic Aperture Radar) images. Based on the water cloud model, Chen model, and Dubois model, we proposed two developed algorithms for soil moisture retrieval and validated their performance using experimental data. We eliminated the effect of vegetation cover by using the water cloud model and minimized the effect of soil surface roughness by solving the Dubois equations. Two experimental campaigns were conducted in the Qinghai Lake watershed, northeastern Tibetan Plateau in September 2012 and May 2013, respectively, with simultaneous satellite overpass. Compared with the developed Chen model, the predicted soil moisture given by the developed Dubois model agreed better with field measurements in terms of accuracy and stability. The RMSE, R2, and RPD value of the developed Dubois model were (5.4, 0.8, 1.6) and (3.05, 0.78, 1.74) for the two experiments, respectively. Validation results indicated that the developed Dubois model, needing a minimum of prior information, satisfied the requirement for soil moisture inversion in the study region.

Classification of crop fields in northeast Thailand based on hydrological characteristics detected by L-band SAR backscatter data

Synthetic aperture radar (SAR) backscatter amplitude image data have proven useful in estimating soil moisture levels and in approximating areas of water inundation over large regions. Based on the pattern of seasonal change in the backscatter coefficient at each image pixel, this study classified a variety of crop fields in Northeast Thailand according to their hydrological characteristics. L-band horizontal-transmit horizontal-receive (HH) polarization images from advanced land observing satellite phased array type L-band synthetic aperture radar (ALOS-PALSAR) at six dates from January to December over the rainy season (May to November) in 2007 were used. Fifteen clusters of pixels were generated using the k-means method, with five variables obtained by taking the difference between the backscatter coefficient for the dry season (January) and the other five dates, effectively removing effects of soil surface roughness. As a result, a detailed spatial distribution of hydrological characteristics that accurately reflected topographical features and hydrological conditions was obtained.

Soil Surface Roughness Effects on Infiltration Process of a Cultivated Slopes on the Loess Plateau of China

Infiltration is the only way water enters soil on the cultivated slopes of the China’s Loess Plateau, so infiltration plays an important role in conserving soil moisture. The objective of this study was to investigate how a soil wetting front created by simulated rainfall migrated in soil with different types of surface roughness. The three types of soil surface treatments studied included surfaces of smooth, medium rough and rough soil. The results showed that, 1) compared with a smooth surface texture, medium rough and rough surface textures have a higher infiltration capacity; 2) the infiltration rate gradually decreases as the wetting front deepens and the rate tends stabilize over time. This change could be described by a logarithmic function; 3) at the early stage of rainfall, the wetting front of medium rough and rough surface textures varied greatly, while the variability of the wetting front decreases markedly after the infiltration rate stabilizes; 4) with increasing depth of the wetting front, the similarity between the wetting front and soil surface profile decreased significantly for the medium rough and rough surface textures. These results indicate that the process of infiltration on cultivated slopes on the Loess Plateau changed from a non-uniform pattern to a uniform pattern as time passed during a rainfall event. Overall, soils with rougher soil surfaces experienced a larger effect of roughness on the process of infiltration.

Effect of soil surface roughness and scene components on soil surface bidirectional reflectance factor

Wang, Z., Coburn, C. A., Ren, X. and Teillet, P. M. 2012. Effect of soil surface roughness and scene components on soil surface BRF. Can. J. Soil Sci. 92: 297–313. Bidirectional Reflectance factor (BRF) data of both rough [surface roughness index (SRI) of 51%] and smooth soil surfaces (SRI of 5%) were acquired in the laboratory under 30° illumination zenith angle using a Specim V10E imaging spectrometer and an Ocean Optics non-imaging spectrometer mounted on the University of Lethbridge Goniometer System version 2.5 (ULGS-2.5) and version 2.0 (ULGS-2.0), respectively. Under controlled laboratory conditions, the rough soil surface exhibited higher spectral reflectance than the smooth surface for most viewing angles. The BRF of the rough surface varied more than the smooth surface as a function of the viewing zenith angle. The shadowing effect was stronger for the rough surface than for the smooth surface and was stronger in the forward-scattering direction than in the backscattering direction. The pattern of the BRF generated with the non-image based data was similar to that generated with the whole region of interest (ROI) of the image-based data, and that of the whole ROI of the image-based data was similar to that of the illuminated scene component. The BRF of the smooth soil surface was dominated by illuminated scene component, i.e., the sunlit pixels, whereas the shaded scene component, i.e., the shaded pixels, was a larger proportion of the BRF of the rough soil surface. The image-based approach allowed the characterization of the contribution of spatial components in the field of view to soil BRF and improved our understanding of soil reflectance.

THE EFFECT OF SURFACE ROUGHNESS ON THE SOIL LOSS

Field measurements and lab analysis were conducted to determine the effect of soil surface roughness ( oriented and random) resulted from discing and chiseling on the soil loss by wind erosion. Some soil ridges parameters were measured in the field and aggregation (clods and non-erodible aggregates) were determined in the lab. The soil loss of the two operations was determined as a function of these variables. The results showed that the large proportions of random roughness(large clods) and clear oriented roughness (soil ridges) in the tilled layer after soil discing have a greatest effect in reducing the amount of soil loss and potential erosivity of soil by wind action in comparison with soil chiseling .This is attributed to the fact that the soil clods easily crushed into fine aggregates after soil chiseling but remained unchanged in soil discing .The statistical analysis coming in agreement with the results of the lab and field investigation

A Parameterized Surface Emission Model at L-Band for Soil Moisture Retrieval

The effects of soil surface roughness play a significant role in the microwave emission from the surface. Therefore, a good parameterization of the effects is a prerequisite for retrieving surface soil moisture information. With recent physical model developments, the advanced integral equation model (AIEM) has been proven to provide accurate representation over a wide range of surface-roughness conditions. We evaluated the capability of the AIEM model in simulating multiangular surface emission signals in comparison with a field experiment data set. A simplified multiangular surface emission model was developed based on simulated database using the AIEM model. Based on the parameterized model, an inversion procedure was developed using dual-polarization microwave brightness temperatures to retrieve soil moisture. Two data sets were used to test the inversion algorithm, and the accuracies in root-mean-square error were about 4% for incidence angles from 20° to 50°. This new simple model is suitable for soil moisture retrieval from future L-band satellite data.

Effect of soil roughness on the inversion of off‐ground monostatic GPR signal for noninvasive quantification of soil properties

We report on a laboratory experiment that investigates the effect of soil surface roughness on the identification of the soil electromagnetic properties from full‐wave inversion of ground‐penetrating radar (GPR) data in the frequency domain. The GPR system consists of an ultrawide band stepped‐frequency continuous‐wave radar combined with an off‐ground monostatic horn antenna. Radar measurements were performed above a rectangular container filled with a loose sandy soil subject to seven water contents and four random surface roughnesses, including a smooth surface as reference. Compared to previous studies, we have reduced the modeling error of the GPR signal for the smooth surface case thanks to improved antenna transfer functions by solving an overdetermined system of equations based on six model configurations instead of only three. Then, the continuously increasing effect of surface roughness on the radar signal with respect to frequency is clearly observed. In close accordance with Rayleigh's criterion, both the radar signal and the inversely estimated parameters are not significantly affected if the surface protuberances are smaller than one eighth of a wavelength. In addition, when this criterion is not respected, errors are made in the estimated parameters, but the inverse solution remains stable. This demonstrates the promising perspectives for application of GPR for noninvasive water content estimation in agricultural and environmental field applications.

Effects of surface conditions on organic matter measurements of a soil sensor

Abstract A set of experiments was conducted to examine the effects of soil surface roughness and surface color discontinuities on a real-time soil organic matter sensor. The purpose of such a soil sensor is to accurately measure soil properties; thus providing information that can be used to set chemical and fertilizer application rates. After examining several chemical application labels it was determined that 1% organic matter error should be considered significant in data used for controlling chemical application. It was hypothesized that soil surface roughness and surface color discontinuities might produce significant errors in the prediction of soil organic matter levels. The effects of surface roughness were examined at two different sensor elevations relative to the average height of the sample profile. Sensor measurements in response to color discontinuities, to approximate non-decomposed plant matter, during calibration procedures was examined. Color discontinuities with a width of 3.0 mm were found to produce measurement errors of more than 6.0% soil organic matter content. It was also found that rough surfaces can produce significantly low organic matter measurements during field use. Both error sources produce artificially low organic matter measurements during field use, while this does reduce the yield potential of a field, it also reduces the potential for water contamination. Calibration samples should be free of all non-decomposed plant matter, and as smooth as possible to prevent artificially high organic matter measurements. Rough samples or samples with non-decomposed plant matter, used during the calibration procedure would result in too much chemicals being applied to a field, increasing water contamination potential.