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

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.

Estimation of Soil Surface Roughness Using Stereo Vision Approach

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.

Wind erosion controls on microplastics from soils: linking soil surface properties with microplastic flux

<p>Recent studies of soils in the Alps and Middle East indicate airborne transport of microplastics following wind erosion may be significant.  Where microplastics have been entrained by wind they show substantial enrichment ratios compared to mineral particle erosion.  Further, microplastic shape affects enrichment ratios with those for fibres greater than for microbeads which may reflect the lower density and asymmetric shape of microplastics compared to soil particles. This suggests that terrestrial to atmospheric transfer of microplastics could be a significant environmental transport pathway. However, currently we have very little understanding of how the properties, in particular the surface characteristics, of the sediment which they are being eroded from affects their entrainment potential.</p><p>This paper reports wind tunnel studies run to explore the impacts of soil surface characteristics on microplastic flux by wind erosion.  Experiments were performed in a boundary layer simulation wind tunnel with an open-loop suction design.  The tunnel has a working section of 12.5m x 0.7m x 0.76m and is housed in an environmental chamber which, for this study, was held constant at 20 <sup>o</sup>C and 20% RH. In experiments two types of low density microplastic (microbeads and fibres) were mixed into a poorly-sorted soil containing 13% organics.  The polyethylene microbeads had a size range of 212-250 microns and density of 1.2 g cm<sup>3</sup> and the polyester fibres were 5000 microns long and 500-1000 microns in width with a density of 1.38 g cm<sup>3</sup>.  Microplastics were mixed into the sediment in concentrations ranging from 40-1040 mg kg<sup>-1</sup>. For each experiment, test surfaces were prepared by filling a 1.0m x 0.35m x  0.025m metal tray with the given mixture of test material which was lowered into the wind tunnel such that it was flush with the tunnel floor and levelled. The wind tunnel was then switched on and run with increasing wind speeds using 0.25 m s<sup>-1</sup> increments until continuous saltation occurred.  Soil surface roughness was scanned prior to and after each experiment using a high resolution laser scanner (0.5mm resolution over the entire test section).  Transported soil and microplastic particles were captured in bulk using a 2 cm wide by 40 cm tall Guelph-Trent wedge trap that was positioned 2 m downwind of the test bed. </p><p>Discussion concentrates on linking the changes in soil surface topography to the magnitude of microplastic flux where data shows that there is a correlation between the development of the soil surfaces and overall microplastic flux.  Specifically, soil surface roughness is seen as a significant control on microplastic flux where it has a greater overall effect on microplastic fibre flux as compared to the microplastic beads.  The outcome of this research is pertinent to developing understanding surrounding the likely controls and hence propensity of microplastics to be entrained from soil by wind erosion. </p>

Method and challenges of tracing soil-surface transport of microplastic particles with an advanced-imaging sCMOS camera

<p>Although the impact of microplastic particles (MPs) in different ecosystems has recently become subject of numerous studies, the knowledge of spatial distribution and transport of MP in terrestrial environments is still limited. While first studies in this field have focused on the abundance of MPs in soils and its vertical distribution, only little is known about the mechanisms of MP transport on the surfaces of sediments and soils. To analyse the interaction between soil surface roughness, inclination and irrigation rate, we investigate MP surface transport mechanisms and patterns by using images of an advanced scientific complementary metal–oxide–semiconductor (sCMOS) high-resolution camera. For this study an experimental set-up including a flume box with several surfaces and an artificial irrigation system was used. In this setup we traced pathways of fluorescent amorphously shaped polystyrene (PS) and Polymethyl methacrylate (PMMA) particles on surfaces of different roughnesses and inclination. Subsequently, time series of the images were analyzed by combining R and Python packages was. This included the calculation of MP particle size, estimation of pathways and path lengths.  Our first results suggest a large influence of the water film thickness and the microrelief of the studied surfaces leading to the creation of preferential pathways for the MP particles.</p>

INVESTIGATION ON DATA COLLECTION AND FRACTAL CHARACTERISTICS OF SOIL SURFACE ROUGHNESS

It is of great significance to acquire the soil surface roughness accurately for the study of the interaction between tractors and soil. Based on the laser sensor, this paper proposed the non-contact measuring instrument of the soil surface roughness with the data acquiring system by using Lab-View software. By using W-M theory, three commonly used fractal dimension calculation methods are compared and analyzed.. The result showed that the Root-mean-square method has the highest accuracy and clear physical meaning, which is ideal method to calculate the soil surface roughness characteristics. When the fractal dimension is between 1.4 and 1.6, the acquired data is analysed by the Root-mean-square method to obtain the fractal features of the soil surface roughness. The experiment results indicated that the fractal dimension of the ploughed surface is 1.39, that of disc harrow surface is 1.550, and that of rolled surface is 1.46-1.54. Obviously, the fractal dimension can accurately distinguish the soil surface roughness with the different treatments. However, the fractal dimension selected from different scales showed an obvious instability during calculations. The surface roughness index combined with the two parameters can effectively represent the soil surface roughness, and the larger the surface roughness index is, the greater the surface roughness is.