Search results for " ROUGH."

Turbulent Flow Structures For Different Roughness Conditions of Channel Walls: Results of experimental investigation in laboratory flumes

2013

How and why does willow biochar increase a clay soil water retention capacity?

2018

Addition of biochar into a soil changes its water retention properties by modifying soil textural and structural properties. In addition, internal micrometer-scale porosity that is able to directly store readily plant available water affects soil water retention properties. This study shows how precise knowledge of the internal micrometer-scale pore size distribution of biochar can deepen the understanding of the biochar-water interactions in soils. The micrometer-scale porosity of willow biochar was quantitatively and qualitatively characterized using X-ray tomography, 3D image analysis and Helium ion microscopy. The effect of biochar application on clay soil water retention was studied by…

Impact of very low crop residues cover on wind erosion in the Sahel

2011

International audience; In the Sahel, with average annual precipitation in the order of 500 mm yr− 1, wind erosion occurs mainly on cultivated millet fields whose surfaces are only partially covered by crop residues. The impact of these residues on wind erosion was not clearly established. The objective of this study is thus to quantify the actual amount of crop residues in traditional Sahelian fields and to determine their impacts on wind erosion by reference to a bare surface throughout the seasonal cycle over several years. At the beginning of the year during dry season, Sahelian farmers use to "clean" their fields, i.e. cut and lay flat on the soil surface any millet stalks still standi…

Roughness evaluation of vine leaf by image processing

2013

International audience; The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The syste…

Morphological and chemical dynamics upon electrochemical cyclic sodiation of electrochromic tungsten oxide coatings extracted by in situ ellipsometry.

2020

The sodiation–desodiation process of sputtered amorphous electrochromic tungsten oxide coatings in an aqueous-based medium was simultaneously monitored over 99 cycles by cyclic voltammetry and in situ spectroscopic ellipsometry. This allowed extracting the evolution of optical and geometrical parameters upon cycling. The resulting electrochemical coloring-bleaching process was dynamically fitted in the 1.8–2.8 eV optical range with a four-phase model including a constrained spline parametrization of the dielectric function. This allows real time access to thickness, surface roughness, and dielectric function of N a x W O 3 . The temporal evolution of the latter in the fully colored state wa…

Measuring vine leaf roughness by image processing

2013

International audience; In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence on pesticide application as nozzle types, liquid viscosity and leaf surface. Specific models have been developed showing that the predominant factor for the leaf is the leaf roughness, because it is related on adhesion mechanisms of li…

Leaf surface roughness characterization by image processing

2013

International audience

Coupling two radar backscattering models to assess soil roughness and surface water content at farm scale

2013

Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and …

Experimental Observation Of Turbulent Structures In A Straight Flume

2010

Experimental and theoretical studies of the structure of turbulence in open-channel flows have shown that the dynamics of the wall layer turbulence is dominated by the formation and growth of turbulent structures which evolve periodically as part of the so-called bursting phenomena. In the present paper experimental results obtained in a straight flume for different roughness conditions of the channel walls are described. The occurrence of turbulent events is verified by applying the conditioned quadrant analysis. The information about the spatial and temporal scales of the events is obtained through the space-time correlations of the conditioned data.

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

2017

Abstract This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (H R , and N Rp , p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and H R  = 0.1, whereas values of ω = 0.06 − 0.08 and H R  = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, H R and N Rp , p = H,V) were tested in this study, tre…