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

2012 ◽  
Vol 92 (2) ◽  
pp. 297-313 ◽  
Author(s):  
Z. Wang ◽  
C. A. Coburn ◽  
X. Ren ◽  
P. M. Teillet
Keyword(s):  
Surface Roughness ◽  
Rough Surface ◽  
Zenith Angle ◽  
Smooth Surface ◽  
Soil Surface ◽  
Bidirectional Reflectance ◽  
Imaging Spectrometer ◽  
Bidirectional Reflectance Factor ◽  
Reflectance Factor ◽  
Soil Surface Roughness

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.

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.

scholarly journals Equipments to manage soil and irrigated rice straw for the sequential sowing of soybean in tropical floodplains1

2019 ◽  
Vol 49 ◽  
Author(s):  
José Geraldo da Silva ◽  
Adriano Stephan Nascente ◽  
Pedro Marques da Silveira
Keyword(s):  
Surface Roughness ◽  
Fuel Consumption ◽  
Rice Straw ◽  
Soil Surface ◽  
Plant Residues ◽  
Operating Speed ◽  
Randomized Design ◽  
Consequent Reduction ◽  
Soil Surface Roughness ◽  
Energetic Performance

ABSTRACT The presence of straw hinders the sowing of soybean cultivated in succession to rice, in areas irrigated by flooding. This study aimed to evaluate the combination of different configurations of a rice harvester and subsequent activities in the operational and energetic demand of rice straw management and in the soil surface roughness, in order to cultivate soybean in succession. Three independent experiments were conducted in a completely randomized design, as well as evaluated the fuel consumption, effective operating speed, working capacity and final surface roughness of the ground. The energy costs of harvesting rice do not increase when the automated harvester operates with a spreader to distribute the straw on the ground and to avoid the formation of furrows. The presence of rice plant residues in the field increases the skidding of the tractor when pulling the knife-roller, with a consequent reduction of the operating speed, but this does not affect the operational capacity and the fuel consumption. The increase in the number of light harrowings, from one to two operations, in areas worked with knife-roller or intermediate harrow, requires more time and fuel in the management of the soil and rice straw, but leaves the ground with less surface roughness. The management system with knife-roller operation and two light harrowings is the most appropriate method to prepare the soil for soybean cultivation after rice, because it provides the best combination of technical and energetic performance.

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