scholarly journals Quantifying the changes of soil surface microroughness due to rainfall impact on a smooth surface

2017 ◽  
Vol 24 (3) ◽  
pp. 569-579 ◽  
Author(s):  
Benjamin K. B. Abban ◽  
A. N. (Thanos) Papanicolaou ◽  
Christos P. Giannopoulos ◽  
Dimitrios C. Dermisis ◽  
Kenneth M. Wacha ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Gaussian Model ◽  
Surface Profile ◽  
Soil Surface ◽  
Soil Conditions ◽  
Agricultural Field ◽  
Quantitative Evidence ◽  
Soil Surface Roughness ◽  
Surface Microroughness

Abstract. This study examines the rainfall-induced change in soil microroughness of a bare smooth soil surface in an agricultural field. The majority of soil microroughness studies have focused on surface roughness on the order of ∼ 5–50 mm and have reported a decay of soil surface roughness with rainfall. However, there is quantitative evidence from a few studies suggesting that surfaces with microroughness less than 5 mm may undergo an increase in roughness when subject to rainfall action. The focus herein is on initial microroughness length scales on the order of 2 mm, a low roughness condition observed seasonally in some landscapes under bare conditions and chosen to systematically examine the increasing roughness phenomenon. Three rainfall intensities of 30, 60, and 75 mm h−1 are applied to a smoothened bed surface in a field plot via a rainfall simulator. Soil surface microroughness is recorded via a surface-profile laser scanner. Several indices are utilized to quantify the soil surface microroughness, namely the random roughness (RR) index, the crossover length, the variance scale from the Markov–Gaussian model, and the limiting difference. Findings show a consistent increase in roughness under the action of rainfall, with an overall agreement between all indices in terms of trend and magnitude. Although this study is limited to a narrow range of rainfall and soil conditions, the results suggest that the outcome of the interaction between rainfall and a soil surface can be different for smooth and rough surfaces and thus warrant the need for a better understanding of this interaction.

Study on the Measurement Method of Soil Surface Roughness

2011 ◽  
Vol 383-390 ◽  
pp. 5357-5362
Author(s):  
Zi Cheng Zheng ◽  
Shu Qin He
Keyword(s):  
Surface Roughness ◽  
Laser Scanner ◽  
Soil Surface ◽  
Poor Correlation ◽  
Roller Chain ◽  
Soil And Water Loss ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
Slope Farmland

Based on the determination method of the comprehensive domestic and international surface roughness, by the method of indoor artificial rainfall, the determination of soil surface roughness had been studied from the measurement accuracy, time-consuming and resolution. The results showed that the laser scanner method was the best to determine the surface roughness, followed by pin meter method, the roller chain meter method, and the ruler was the worst. The results of determination had the better correlation between the laser scanner method and the roller chain meter method before rainfall, however they had poor correlation after rainfall. They had the better correlation between the laser scanner method and pin meter method both before rainfall and after rainfall. And on this basis, the relationships were established among the different methods. The results provide theory basis for the further study on soil surface roughness. At the same time, it would serve for harnessing soil and water loss of the slope farmland in Loess Plateau.

scholarly journals Quantifying the changes of soil surface microroughness due to rainfall-induced erosion on a smooth surface

2017 ◽  
Author(s):  
Benjamin K. B. Abban ◽  
A. N. Thanos Papanicolaou ◽  
Christos P. Giannopoulos ◽  
Dimitrios C. Dermisis ◽  
Kenneth M. Wacha ◽  
...  
Keyword(s):  
Smooth Surface ◽  
Laser Scanner ◽  
Surface Profile ◽  
Soil Surface ◽  
Bare Soil ◽  
Agricultural Landscapes ◽  
Length Scales ◽  
Surface Conditions ◽  
Consistent Increase ◽  
Surface Microroughness

Abstract. This study examines the rainfall induced change in soil microroughness of a bare soil surface in agricultural landscapes. The focus is on the quantification of roughness length under the action of rainfall for initial microroughness length scales of 2 mm or less, defined here as initial smooth surface conditions. These conditions have not been extensively examined in the literature as most studies have focused on initial disturbed surface conditions (bed surface conditions with initial length scales greater than 2 mm and varying between 5–50 mm). Three representative intensities namely 30 mm/h, 60 mm/h and 75 mm/h were applied over a smoothened bed surface at a field plot via a rainfall simulator. Soil surface microroughness measurements were obtained via a surface-profile laser scanner. Two indices were utilized to quantify soil surface microroughness, namely the Random Roughness (RR) index and the crossover length. Findings show a consistent increase in roughness under the action of rainfall for initial microroughness length scales of 2 mm. This contradicts existing literature where a monotonic decay of roughness of soil surfaces with rainfall is recorded for disturbed surfaces. Analysis shows that on an average the RR and the crossover length post run increase by a multiple of 3.15 and 1.9, respectively from their corresponding values apriori the runs.

scholarly journals Application of Sentinel-1B Polarimetric Observations to Soil Moisture Retrieval Using Neural Networks: Case Study for Bare Siberian Chernozem Soil

2021 ◽  
Vol 13 (17) ◽  
pp. 3480
Author(s):  
Konstantin Muzalevskiy ◽  
Anatoly Zeyliger
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Soil Surface ◽  
Weather Conditions ◽  
Agricultural Field ◽  
Wide Range ◽  
Soil Surface Roughness ◽  
The Relationship

Sentinel-1 is currently the only synthetic-aperture radar, which radar measurements of the earth’s surface to be carried out, regardless of weather conditions, with high resolution up to 5–40 m and high periodicity from several to 12 days. Sentinel-1 creates a technological platform for the development of new globally remote sensing algorithms of soil moisture, not only for hydrological and climatic model applications, but also on a single field scale for individual farms in precision farming systems used. In this paper, the potential of soil moisture remote sensing using polarimetric Sentinel-1B backscattering observations was studied. As a test site, the fallow agricultural field with bare soil near the Minino village (56.0865°N, 92.6772°E), Krasnoyarsk region, the Russian Federation, was chosen. The relationship between the cross-polarized ratio, reflectivity, and the soil surface roughness established Oh used as a basis for developing the algorithm of soil moisture retrieval with neural networks (NNs) computational model. Two NNs is used as a universal regression technique to establish the relationship between scattering anisotropy, entropy and backscattering coefficients measured by the Sentinel-1B on the one hand and reflectivity on the other. Finally, the soil moisture was found from the soil reflectivity in solving the inverse problem using the Mironov dielectric model. During the field campaign from 21 May to 25 August 2020, it was shown that the proposed approach allows us to predict soil moisture values in the layer thickness of 0.00–0.05 m with the root-mean-square error and determination coefficient not worse than 3% and 0.726, respectively. The validity of the proposed approach needs additional verification on a wider dataset using soils of different textures, a wide range of variations in soil surface roughness, and moisture.

scholarly journals Effect of tillage, slope, and rainfall on soil surface microtopography quantified by geostatistical and fractal indices during sheet erosion

2020 ◽  
Vol 12 (1) ◽  
pp. 232-241
Author(s):  
Na Ta ◽  
Chutian Zhang ◽  
Hongru Ding ◽  
Qingfeng Zhang
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Soil Surface ◽  
Rainfall Events ◽  
Tillage Practices ◽  
Surface Microtopography ◽  
Artificial Rainfall ◽  
Soil Surface Roughness ◽  
The Difference ◽  
Sheet Erosion

AbstractTillage and slope will influence soil surface roughness that changes during rainfall events. This study tests this effect under controlled conditions quantified by geostatistical and fractal indices. When four commonly adopted tillage practices, namely, artificial backhoe (AB), artificial digging (AD), contour tillage (CT), and linear slope (CK), were prepared on soil surfaces at 2 × 1 × 0.5 m soil pans at 5°, 10°, or 20° slope gradients, artificial rainfall with an intensity of 60 or 90 mm h−1 was applied to it. Measurements of the difference in elevation points of the surface profiles were taken before rainfall and after rainfall events for sheet erosion. Tillage practices had a relationship with fractal indices that the surface treated with CT exhibited the biggest fractal dimension D value, followed by the surfaces AD, AB, and CK. Surfaces under a stronger rainfall tended to have a greater D value. Tillage treatments affected anisotropy differently and the surface CT had the strongest effect on anisotropy, followed by the surfaces AD, AB, and CK. A steeper surface would have less effect on anisotropy. Since the surface CT had the strongest effect on spatial variability or the weakest spatial autocorrelation, it had the smallest effect on runoff and sediment yield. Therefore, tillage CT could make a better tillage practice of conserving water and soil. Simultaneously, changes in semivariogram and fractal parameters for surface roughness were examined and evaluated. Fractal parameter – crossover length l – is more sensitive than fractal dimension D to rainfall action to describe vertical differences in soil surface roughness evolution.

scholarly journals Estimation of Soil Surface Roughness Using Stereo Vision Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4386
Author(s):  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
Tarahom Mesri-Gundoshmian ◽  
Aitazaz A. Farooque ◽  
Hassan Afzaal
Keyword(s):  
Surface Roughness ◽  
Stereo Vision ◽  
Agricultural Soils ◽  
Depth Map ◽  
Soil Surface ◽  
Regression Equations ◽  
Height Profile ◽  
Peak Fitting ◽  
Soil Surface Roughness ◽  
Tillage Operations

Soil roughness is one of the most challenging issues in the agricultural domain and plays a crucial role in soil quality. The objective of this research was to develop a computerized method based on stereo vision technique to estimate the roughness formed on the agricultural soils. Additionally, soil till quality was investigated by analyzing the height of plow layers. An image dataset was provided in the real conditions of the field. For determining the soil surface roughness, the elevation of clods obtained from tillage operations was computed using a depth map. This map was obtained by extracting and matching corresponding keypoints as super pixels of images. Regression equations and coefficients of determination between the measured and estimated values indicate that the proposed method has a strong potential for the estimation of soil shallow roughness as an important physical parameter in tillage operations. In addition, peak fitting of tilled layers was applied to the height profile to evaluate the till quality. The results of this suggest that the peak fitting is an effective method of judging tillage quality in the fields.

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