Soil Surface Characteristics Research Articles

Raindrop-impact-induced ejection characteristics of surface particles for soils with a textural gradient

The ejection of surface soil particles caused by raindrops is an important stage of water erosion. Generally, surface soil is typically comprised of more complex structural units (e.g. crumb, block, or platy structures) rather than single granular structures (e.g. aggregate), especially on surface soil influenced by the different arrangement mode between soil particles. Thus, soil aggregate samples as a selected test material cannot properly characterise the particle ejection characteristics of surface soil with more complex structural units during rainfall. Considering this issue, undisturbed soil samples with a textural gradient were collected as test materials for this study and a needle-type raindrop simulator was used to simulate rainfall experiments. Our results demonstrate that, the first 6 min of rainfall is the key period for particle ejection. During this phase of the experiment, the clay-sized and fine-silt-sized particles were preferentially ejected, and the enrichment ratio (ER) of those particles decreased progressively from sandy loam to silty clay loam. With increased rainfall duration, the soils with a low clay content (e.g. sandy loam, silty loam and loam) were found to develop surface crusts more easily compared with clay loam and silty clay loam. Although the formation of soil crust reduced the size-selectivity of the splash-detached particles, the percentage of sand-sized particles in splash-detachment particles showed an increasing trend, while the percentage of clay-sized and fine-silt-sized particles showed a decreasing trend. The finding of this study suggests that identifying changes in the soil surface condition, as determined by soil texture, is the key factor affecting the ejection characteristics of surface soil particles during splash erosion.

Informativeness of the Long-Term Average Spectral Characteristics of the Bare Soil Surface for the Detection of Soil Cover Degradation with the Neural Network Filtering of Remote Sensing Data

The long-term spectral characteristics of the bare soil surface (BSS) in the BLUE, GREEN, RED, NIR, SWIR1, and SWIR2 Landsat spectral bands are poorly studied. Most often, the RED and NIR spectral bands are used to analyze the spatial heterogeneity of the soil cover; in our opinion, it is outmoded and seems unreasonable. The study of multi-temporal spectral characteristics requires the processing of big remote sensing data based on artificial intelligence in the form of convolutional neural networks. The analysis of BSS belongs to the direct methods of analysis of the soil cover. Soil degradation can be detected by ground methods (field reconnaissance surveys), modeling, or digital methods, and based on the remote sensing data (RSD) analysis. Ground methods are laborious, and modeling gives indirect results. RSD analysis can be based on the principles of calculation of vegetation indices (VIs) and on the BSS identification. The calculation of VIs also provides indirect information about the soil cover through the state of vegetation. BSS analysis is a direct method for analyzing soil cover heterogeneity. In this work, the informativeness of the long-term (37 years) average spectral characteristics of the BLUE, GREEN, RED, NIR, SWIR1 and SWIR2 bands of the Landsat 4–8 satellites for detecting areas of soil degradation with recognition of the BSS using deep machine learning methods was estimated. The objects of study are the spectral characteristics of kastanozems (dark chestnut soils) in the south of Russia in the territory of the Morozovsky district of the Rostov region. Soil degradation in this area is mainly caused by erosion. The following methods were used: retrospective monitoring of soil and land cover, deep machine learning using convolutional neural networks, and cartographic analysis. Six new maps of the average long-term spectral brightness of the BSS have been obtained. The information content of the BSS for six spectral bands has been verified on the basis of ground surveys. The informativeness was determined by the percentage of coincidences of degradation facts identified during the RSD analysis, and those determined in the field. It has been established that the spectral bands line up in the following descending order of information content: RED, NIR, GREEN, BLUE, SWIR1, SWIR2. The accuracy of degradation maps by band was determined as: RED—84.6%, NIR—82.9%, GREEN—78.0%, BLUE—78.0%, SWIR1—75.5%, SWIR2—62.2%.

Assessing the Effect of Intensive Agriculture and Sandy Soil Properties on Groundwater Contamination by Nitrate and Potential Improvement Using Olive Pomace Biomass Slag (OPBS)

The relationship between agricultural activities, soil characteristics, and groundwater quality is critical, particularly in rural areas where groundwater directly supplies local people. In this paper, three agricultural sandy soils were sampled and analyzed for physicochemical parameters such as pH, water content, bulk density, electrical conductivity (EC), organic matter (OM), cation exchange capacity (CEC), and soil grain size distribution. Major and trace elements were analyzed by inductively coupled plasma-optical emission spectrometry (ICP/OES) to determine their concentrations in the fine fraction (FF) of the soils. Afterward, the elemental composition of the soils was identified by X-ray powder diffraction (XRD) and quantified by X-ray fluorescence (XRF). The surface soil characteristics were determined by the Brunauer–Emmett–Teller (BET) method, whereas the thermal decomposition of the soils was carried out using thermogravimetric analysis and differential scanning calorimetric (TGA-DSC) measurements. The morphological characteristics were obtained by scanning electron microscopy (SEM). Afterward, column-leaching experiments were conducted to investigate the soil’s retention capacity of nitrate (NO−3). Parallelly, a chemical and physical study of olive pomace biomass slag (OPBS) residue was carried out in order to explore its potential use as a soil additive and improver in the R’mel area. The OPBS was characterized by physicochemical analysis, assessed for heavy metals toxicity, and characterized using (XRD, XRF, SEM, and BET) techniques. The results show that the R’mel soils were slightly acidic to alkaline in nature. The soils had a sandy texture with low clay and silt percentage (<5% of the total fraction), low OM content, and weak CEC. The column experiments demonstrated that the R’mel irrigated soils have a higher tendency to release large amounts of nitrate due to their texture and a higher degree of mineralization which allows water to drain quickly. The OPBS chemical characterization indicates a higher alkaline pH (12.1), higher water content (7.18%), and higher unburned carbon portion (19.97%). The trace elements were present in low concentrations in OPBS. Macronutrients in OPBS showed composition rich in Ca, K, and Mg which represent 10.59, 8.24, and 1.56%, respectively. Those nutrients were quite low in soil samples. Both XRD and XRF characterization have shown a quasi-dominance of SiO2 in soil samples revealing that quartz was the main crystalline phase dominating the R’mel soils. Oppositely, OPBS showed a reduced SiO2 percentage of 26,29% while K, Ca, and P were present in significant amounts. These results were confirmed by XRF analysis of OPBS reporting the presence of dolomite (CaMg, (CO3)2), fairchildite (K2Ca (CO3)2), and free lime (CaO). Finally, the comparison between the surface characteristic of OPBS and soils by BET and SEM indicated that OPBS has a higher surface area and pore volume compared to soils. In this context, this study suggests a potential utilization of OPBS in order to (1) increase soil fertility by the input of organic carbon and macronutrients in soil; (2) increase the water-holding capacity of soil; (3) increase soil CEC; (4) stabilize trace elements; (5) enhance the soil adsorption capacity and porosity.

RAINWATER RETENTION SITE ASSESSMENT FOR URBAN FLOOD RISK REDUCTION AND FLOOD DEFENCE IN MANDAUE CITY, PHILIPPINES

Abstract. This study utilized a geospatial approach to identify suitable sites for rainwater retention areas in Mandaue City, a highly urbanized city in Cebu, Philippines. Based on the integrated use of remote sensing and Geographic Information System (GIS), ideal sites for rainwater retention were produced to divert surface water runoff from flooding the streets and obstructing traffic. Ideally, the chosen sites should have low infiltration capacity and be within open spaces. For the hydrological planning of the study, we considered the amount of surface runoff, elevation, soil characteristics, land use, and present drainage status of the study area. The Digital Elevation Model (DEM) derived from LiDAR technology is used to obtain the slope raster and generate small streams. Land cover and curve number (CN) of the area were used to compute the surface runoff. The soil textures were converted based on their infiltration rate and runoff potential. Finally, the weighted overlay tool in GIS created a layer identifying potential areas for rainwater retention sites. The findings indicate that 54.8% of the research area was ideal for water harvesting, with open land areas making up 2.3% of the best locations. In addition, a major portion of the appropriate locations lies in clay loam soils and within 0–25 meters in elevation. These results will hugely benefit policymakers and urban planners in helping create solutions to the city’s flooding problem and the looming water crisis.

Fine-scale analysis of edge effect of shrub patch in different grassland types

Boundaries may have important effects on landscape patterns, landscape change mechanisms, and dynamic processes. However, little is known about the dynamic mechanism of patch boundary changes at a fine scale. To elucidate the characteristics of grassland patches at fine scales and to provide a reference for the mechanism of change and development direction of patchy landscapes. In this paper, the patch of different grassland types in Xilingol League was studied by NMDS, RDA, and SEM methods, to analyze the vegetation community and soil characteristics of surface soil and the relationship between them: The changes in soil vegetation community and soil characteristics were completely different among the three grassland types, and the abrupt changes of vegetation index and soil properties were different. Vegetation index mostly ranged from −1 m to 0 m, and soil index mostly ranged from −0.5 m to 1 m. Fine-scale vegetation and soil boundaries are well defined, vegetation boundaries are mostly between −1 and 0 m and soil boundaries are mostly between −0.5–1 m, and soil properties have a clear influence on plant characteristics. The difference in organic matter, nitrogen, and phosphorus content is an important factor affecting the change of patch boundary, the distribution of the RDA results showed that the organic matter, nitrogen and phosphorus contents in all three grasslands explained >70% of the environmental factors. The emergence of annual vegetation involves a process of succession, specifically, the nature of the underlying soil determines the type of plants at the boundary. The dynamic characteristics of the soil-plant mutual-feed mechanism determine the location and variation of patch boundaries to adapt to disturbance states. The results of this study provide insight into how boundaries respond to changes in environmental conditions and drive dynamic changes at the landscape level.

Restoring the iconic Ulmus americana to urban landscapes: Early tree growth responds to aboveground conditions

Native trees provide a range of benefits, from supporting native wildlife to climate regulation, and many urban natural resource managers prioritize native tree planting and restoration. Ulmus americana (American elm) was once widely planted in American cities but has been decimated by Dutch elm disease (DED; Ophiostoma ulmi). Our study evaluated U. americana establishment and growth across urban landscapes. We planted ramets from three DED-tolerant U. americana genotypes (RV16, RV474, and Sunfield) along an urbanization gradient in Newark, DE and Philadelphia, PA, and assessed physiological and morphological responses. We analyzed how U. americana clone growth, chlorophyll fluorescence, and foliar chemistry relate to impervious surface area, ozone (O3) concentrations, and soil characteristics. The one-year post planting mortality rate was low (4%) demonstrating these elms can withstand urban environmental conditions when provided ample water supply and protection from deer. As expected, the elms differed in growth rate, chlorophyll fluorescence, and foliar chemistry between the cities. Elms planted in Philadelphia had greater photosynthetic capacity in July (Fv/Fm = 0.76) and September (Fv/Fm = 0.75), while Newark elms had greater photosynthetic capacity in August (Fv/Fm = 0.78). Depleted foliar δ13C signatures in Philadelphia suggest elms are experiencing greater fossil-fuel derived atmospheric CO2 than in Newark, possibly contributing to the greater growth rates observed in Philadelphia compared to Newark. Enriched foliar δ15N and greater foliar %N in Philadelphia clones suggest they are experiencing greater N deposition from NOx-derived sources compared to Newark clones. Clones growing in Philadelphia had greater foliar nutrient concentrations despite growing in soils with greater heavy metal concentrations. These foliar-soil chemistry patterns suggest that clones growing in Philadelphia respond positively to urban environmental conditions in a large city, whereas clones growing in Newark may be experiencing N limitation in the first year of growth after planting.

Soil surface roughness modelling with the bidirectional autocorrelation function

Surface roughness is a major part of soil surface condition. It results from tillage operations and weathering. Surface roughness parameterisation is still a scientific lock and the object of many studies. An efficient parametrisation of soil surface roughness by modelling the bidirectional autocorrelation function estimated from 2.5D digital elevation models of soil surfaces is introduced. It not only provides geostatistical parameters that can be related to other soil surface characteristics, but let us emphasise that it reproduces the autocorrelation function with very good accuracy. The autocorrelation function is often modelled by a function of three parameters, the height variance, a single correlation length, and a roughness exponent. We added two parameters in order to take into account the anisotropy of soil surfaces and to align the coordinate system in the direction of the maximum correlation length. We propose the way to estimate roughness parameters and show its robustness for soil surfaces using laboratory tests with repeated rainfall events. One soil surface evolves from isotropy to anisotropy, and the other undergoes a reduction of initial anisotropy. The improvement brought by a second correlation length is thus highlighted. Under rainfall impact, the variation of the correlation lengths is more marked than that of the usual roughness parameter that is the root mean squared of the heights. Both parameters are complementary, capturing horizontal or vertical variation respectively. The evolution of the roughness exponent showed a slight increasing trend, which can be related to surface smoothing.

Variations in soil detachment by rill flow during crop growth stages in sloping farmlands on the Loess Plateau

Near soil-surface characteristics change significantly with crop growth in sloping farmlands and thereby induce the temporal variations in soil detachment by rill flow. In this study, variations in soil detachment capacity by rill flow (Dc) and soil erosion resistance to rill flow as reflected by rill erodibility (Kr) and critical shear stress (τc) during crop growth stages were quantified in sloping farmlands of the Loess Plateau, China. The soil samples were collected from four different plots (one control and three crop plots—soybean, corn, and millet) and subjected to flume experiments under different shear stress (τ) levels (5.16–15.61 Pa). The near soil-surface characteristics including soil moisture content (SMC), soil bulk density (BD), soil cohesion (Coh), mean weight diameter (MWD), soil organic matter content (SOM), root length density (RLD), root surface area density (RSAD), and root mass density (RMD), were measured to explain the variations in soil detachment during crop growth stages. The results showed that SMC fluctuated greatly, whereas BD, Coh, MWD, SOM, RLD, RSAD, and RMD increased gradually with crop growth. Measured Dc and fitted Kr significantly decreased with crop growth during one growing season. The mean values of Dc for soybean, corn, and millet decreased by 61.19–67.23%, Kr decreased by 55.53–61.36%, and τc increased by 16.0–18.31% compared to those for the bare soil control. The decrease in Dc and Kr were significantly affected by BD, Coh, SOM, MWD and RLD, RSAD, and RMD (p < 0.01). Seasonal variations in Dc were well simulated by τ, BD, Coh, and RLD (R2 = 0.96, NSE = 0.96). The findings demonstrate that it is essential to consider the seasonal variations in soil detachment for optimizing soil and water conservation measures in sloping farmlands.

Dynamic parameterization of soil surface characteristics for hydrological models in agricultural catchments

The detrimental impacts of surface runoff and soil erosion, particularly in cultivated areas, call for the use of distributed runoff and soil erosion models with a view to supporting adapted catchment management strategies. However, runoff model parameterization remains challenging in agricultural catchments due to the high spatial and seasonal variability of soil properties. Data acquisition is demanding and may not always be feasible. Therefore, model parameterization in such environments have been the subject of numerous research efforts. The combined analysis of land use management and soil surface state was proposed in literature to address this issue and demonstrated its potential for runoff analysis and modelling. However, these research findings were related to specific rainfall sequences and/or soil surface state. In this study, existing knowledge on soil surface state and its application to runoff model parameterization were synthetized and included in an easy-to-use parameterization software (PREMACHE), providing a framework for modelers lacking of means and/or data for modelling complex agricultural catchments.To develop and evaluate the software, a dataset was acquired over 9 years on more than 110 plots in a 1045 ha agricultural catchment, including crop types, soil surface state, rainfall and runoff time series. Soil surface state dynamics was modeled based on crop types and daily rainfall. It was evaluated in the experimental catchment and validated in a nearby catchment. Soil hydrodynamic properties (e.g. infiltration capacity) were deduced from this framework and literature data at a daily time step, for each plots. Moreover, runoff events were measured when the modeled infiltration capacity was low, indicating that the parametrization adequately captured its temporal dynamics. The software developed in this study, as well as setup values deduced from the monitoring campaigns are provided with the manuscript for application in other ungauged catchments and explore their impact on agricultural catchment hydrological dynamics.

Effects of vegetation restoration on carbonate‐derived laterite erodibility in karst mountain areas

AbstractVegetation restoration can exert a profound influence on surface soil characteristics, which likely plays a significant role in the evolution of soil erodibility. This study was conducted to quantify the effects of vegetation restoration on the soil erodibility of carbonate‐derived laterite in karst areas. Experiments were conducted at two representative sites for different years of vegetation restoration. Six karst mountain land use types [grassland (Y1), shrubland (Y2) and woodland (Y3) in young forest (YF); forest trails (O1), shrubland (O2) and woodland (O3) in old forest (OF)]. A structural equation model and random forest regression analysis were applied to distinguish the key environmental factors dominating soil erodibility. The results showed that the soil erodibility K factor exponentially decreased with increasing restoration age and tended to remain stable after 20 years (y = 0.0466 + 0.0316e−0.2889x, R2 = 0.98). The K factor in the OF decreased by an average of 2.1%–14.8% compared to those in the YF. A structural equation model indicated that vegetation restoration indirectly reduced soil erodibility by altering plant growth and coverage and its associated soil property changes. A random forest regression analysis of global datasets indicated that soil erodibility is mainly affected by topographical factors (i.e., latitude, elevation and slope), and their mean decrease accuracy values (IncMSE) exceeded 20%. Both forestland and shrubland were not considered as the most important factors for affecting global soil erodibility. Conversely, grassland, cropland and bare land were among the important factors. These results could guide the potential benefits of vegetation restoration strategies to meet soil conservation and ecosystem function goals in karst mountain ecosystems.

Sentinel-2 images to assess soil surface characteristics over a rainfed Mediterranean cropping system

Soil surface characteristics (SSCs) are of high importance for water infiltration processes in crop fields. As SSCs present strong spatiotemporal variability influenced by climatic conditions and agricultural practices, their monitor has already been explored by using UAV images and multispectral remote sensing. However, each technique has encountered difficulties characterizing this spatiotemporal variability. The objective of this work was to explore the potential of Sentinel-2 images to assess three SSCs – the green vegetation fraction, dry vegetation fraction and physical soil surface structure – at several dates. This work explored two approaches for classifying these three SSCs from five Sentinel-2 images acquired from August to November 2016. In the “single-date” approach, a Random Forest Classifier (RFC) model was trained to classify one SSCj from a dataset extracted from one Sentinel-2 image i (model noted RF_sdi,SSCj). In the “multi-date” approach, a RFC model was trained to classify one SSCj from a dataset extracted from the five Sentinel-2 images (noted RF_mdSSCj). The classification analysis showed that i) the RF_sdi,SSCj and RF_mdSSCj models provided accurate performances (overall accuracy > 0.79) regardless of the studied SSCj and the tested Sentinel-2 image, ii) the RF_sdi,SSCj model did not allow the classification of SSC classes that were not observed on the studied date, and iii) the RF_mdSSCj model allowed the classification of all SSC classes observed in the five Sentinel-2 images. This indicated that several Sentinel-2 images can favourably be used to increase knowledge of spatiotemporal representation of SSCs by extending results of infrequent, localized and cumbersome field work.

Slope position and biochar influence soil properties and seed displacement in a tropical agroecosystem

AbstractSoil erosion due to land use change is a major concern in agricultural areas. There is an urgent need to develop appropriate agricultural practices that promote higher biodiversity and improve soil properties. Little is known about the processes of natural vegetation recovery after disturbance by human activities in tropical humid climates. This is particularly the case for dispersion by water (hydrochory) that affects seeds. The displacement of native plant seeds on the soil surface by runoff may be influenced by soil properties and by the use of biochar in agriculture. As these effects have not been documented in tropical agroecosystems and on steep slopes, we present here an in‐field experiment on a steeply sloping maize field affected by rainfall and tillage erosion in Northern Thailand. Our objectives were to test the effect of catena position and biochar use on seed displacement, soil loss and nutrient losses. We used 24 plots of 1 m2 located at four positions in the catena. Two treatments were tested: (1) conventional system with tillage and (2) biochar incorporated into the soil. We measured the displacement of plastic beads (to imitate round seeds), runoff volume, soil and nutrient losses and soil surface features during in‐situ rainfall simulations. After two simulated rainfall events and four natural rainfall events, total bead exportation was 7.2%. We found that catena position and biochar had an impact on runoff, soil and nutrient losses. In this study, previously incorporated biochar reappeared at the soil surface after 8 months under natural conditions and acted like a crust at the soil surface. This biochar crust increased runoff, increased potassium concentration and decreased phosphorus concentration in runoff, decreased bead displacement and had no effect on soil loss. This study, under tropical climate and steep slope conditions, highlights differences in soil surface features and runoff along the catena. We should consider catena position for improving soil management and using appropriate agroecological practices.Highlights The effect of catena position and biochar use on seed displacement, soil loss and nutrient losses were tested. These effects have not been documented in tropical agroecosystems and steep slopes. Catena position and biochar use had an impact on runoff, soil and nutrient losses. Catena position for improving soil management and using agroecological practices should be considered.

Layered Ejecta Craters in the Candidate Landing Areas of China's First Mars Mission (Tianwen‐1): Implications for Subsurface Volatile Concentrations

AbstractOne important scientific objective of China's first Mars mission (Tianwen‐1) is to investigate the martian surface soil characteristics and water‐ice distribution. Layered ejecta craters (LECs) on Mars are characterized by their fluidized ejecta deposits which have been interpreted to indicate volatiles in the substrate where the impact occurred. Thus, they have potential to serve as important planetary exploration sites/targets of geological significance. In this study, we estimated the absolute model ages (AMAs) of 68 LECs with diameters &gt;4 km using the Context Camera (an instrument onboard the Mars Reconnaissance Obiter, MRO) images in the two candidate landing areas of Tianwen‐1 mission, aiming to reveal temporal‐spatial variation of the regional subsurface volatiles, in particular water ice. The results from a combination of the derived AMAs and the ejecta mobility (EM) values for the southern Utopia‐Isidis Planitia (SUIP) and southern Chryse Planitia (SCP) show that the LECs in both regions plausibly have formed throughout the Amazonian and Hesperian periods. However, the subsurface volatile concentrations might have undergone different evolution histories: SUIP shows a relatively stable trend with time, while there seems a general downward trend in SCP. The presence of the fresh ∼1.5‐km‐diameter LECs suggests that volatile concentrations might have been at depth less than 150 m. We conclude with a discussion of recommendations for Tianwen‐1 and future explorations that have the ability to effectively detect any signs of past and present water/ice on Mars, which contain a wealth of information of past and recent climate.

Contemporary and future dust sources and emission fluxes from gypsum- and quartz-dominated eolian systems, New Mexico and Texas, USA

AbstractRecent research on dust emissions from eolian dunes seeks to improve regional and global emissions estimates and knowledge of dust sources, particularly with a changing climate. Dust emissions from dune fields can be more accurately estimated when considering the whole eolian system composed of active to stabilized dunes, interdunes, sand sheets, and playas. Each landform can emit different concentrations of dust depending on the supply of silt and clay, soil surface characteristics, and the degree to which the landforms are dynamic and interact. We used the Portable In Situ Wind Erosion Laboratory (PI-SWERL) to measure PM10 (particulate matter &amp;lt;10 μm) dust emission potential from landforms in two end-member eolian systems: the White Sands dune field in New Mexico (USA), composed of gypsum, and the Monahans dune field in west Texas, composed of quartz. White Sands is a hotspot of dust emissions where dunes and the adjacent playa yield high dust fluxes up to 8.3 mg/m2/s. In contrast, the active Monahans dunes contain 100% sand and produce low dust fluxes up to 0.5 mg/m2/s, whereas adjacent stabilized sand sheets and dunes that contain silt and clay could produce up to 17.7 mg/m2/s if reactivated by climate change or anthropogenic disturbance. These findings have implications for present and future dust emission potential of eolian systems from the Great Plains to the southwestern United States, with unrealized emissions of &amp;gt;300 t/km2/yr.

Field test of the surface soil moisture mapping using Sentinel-1 radar data

Soil surface moisture is one of the key parameters for describing the hydrological state and assessing the potential availability of water for irrigated plants. Because the radar backscattering coefficient is sensitive to soil moisture, the application of Sentinel-1 data may support soil surface moisture mapping at high spatial resolution by detecting spatial and temporal changes at the field scale for precision irrigation management. This mapping is required to control soil water erosion and preferential water flow to improve irrigation water efficiency and minimise negative impacts on surface and ground water bodies.Direct observations of soil surface moisture (5-cm thickness) were performed at an experimental plot in the study site of the All-Russian Scientific Research Institute of Irrigated Agriculture, near the village Vodnyy, Volgograd region. Soil surface moisture retrieval from Sentinel-1 was performed at the same location. A second set of soil surface moisture was calculated for the soil sampling sites using the permittivity model, based on the estimates of soil surface characteristics: a) reflectivity, obtained by the neural network method from Sentinel-1 observations; b) roughness, obtained from the geodata of the stereoscopic survey with unmanned aerial vehicle Phantom 4 Pro.The raster set of soil surface moisture geodata was obtained based on the reflectivity geodata raster set to solve the inverse problem using a permittivity model that considers the soil texture of the experimental plot. The determination coefficient (0.948) and standard deviation (2.04%) were obtained by comparing both sets of soil moisture point geodata taken from the same soil sampling sites. The values confirmed a satisfactory linear correlation between the directly measured and indirectly modelled sets. A comparison of the two sets of geodata indicated a satisfactory reproduction of the first set by the second set.As a result, the developed method can be considered as the scientific and methodological basis of the new technology of soil surface moisture monitoring by radar, which is one of the basic characteristics used in precision irrigation management.

Environmental drivers of taxonomic and phylogenetic diversity patterns of plant communities in semi-arid steppe rangelands of North Africa

Since phytoecological and biodiversity studies deepen our understanding on ecosystem functioning and habitat health status of rangelands, this study analyzed the composition and plant taxonomic and phylogenetic diversity of semi-arid steppe vegetation communities in rangelands of northeastern Algeria. Based on 80 floristic samples, vegetation was analyzed using various species diversity parameters (incidence- and abundance-based diversity and similarity indices, rarefaction and extrapolation estimations). Sampling totaled 75,197 plant individuals that were classified into 63 species, 55 genera and 21 families, with predominance of Asteraceae (33.3%) and Fabaceae (14.3%) species. Rarefaction and extrapolation curves applied for the whole steppe vegetation showed that the expected species richness was stable and no rare species could be found when increasing the sample size. Generalized linear models showed that the variation of plant diversity parameters (alpha-biodiversity) was significant among the phytoecological groups individualized. The multiple factor analysis demonstrated that inter-relationships between the phytoecological groups result from characteristics of soil surface, vegetation indicators, and dominant plant species that influenced plant diversity indices. The qualitative and quantitative analysis of similarity (beta-biodiversity) revealed high resemblances (>50%) between these groups, indicating a spatial homogenization of plant communities, which are dominated by annual species. The variation of phylogenetic diversity, estimated using taxonomic structures (i.e. genus/species and family/species ratios) was related to local environmental conditions. Phylogenetic diversity increased with the increase in total vegetation cover and the average height of Halfa grass (Stipa tenacissima), but it was negatively correlated with land degradation indicators such as sand deposit sheets, bedrock outcrop, coarse-grained materials, and plant necromass or vegetation litter). This study demonstrated that despite the recorded plant diversity, the quality of rangelands in the study area was of a therophytic character (substantial level of annuals), indicating a state of land and vegetation degradation in rangelands of northeastern Algeria.

Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

Soil erodibility indicators as affected by water level fluctuations in the Three Gorges Reservoir area, China

The near soil surface characteristics of plant communities probably change considerably with seasonal water-level fluctuations, thus induced corresponding variations in soil erodibility indicators. This study was conducted to investigate the variations in soil erodibility indicators triggered by varying inundation intensity or temporal water-level fluctuations in the Three Gorges Reservoir area. Two non-inundation sites (as the control) and six sites with different water-level fluctuation intensities were selected to measure or calculate eight soil erodibility indicators and then to determine a comprehensive soil erodibility index (CSEI). The erodibility indicators included K factor, slaking rate (SR), mean weighted diameter of aggregates (MWD), mean number of waterdrop impacts (MND), structural stability index (SSI), saturated conductivity (Ks), cohesion (Coh), and penetration resistance (PR). The variation in each soil erodibility indicator could be explained by the changes in near soil surface characteristics of plant community. The results indicated that the MWD, MND, SR, Coh, SSI, and Ks decreased gradually with the intensity of water level fluctuations (IWLFs), while the reverse was found for PR. K factor firstly increased and then decreased with the IWLFs. The CSEI increased with IWLFs, which was closely related to elevation. Compared with the control, CSEI increased by 123.3%, 132.9%, 93.9%, 84.8%, 48.7%, and 55.2% for water levels of 150, 155, 160, 165, 170 and 175 m, respectively. The changes in near soil surface characteristics of the plant community directly led to differences in soil erodibility indicators. Significant positive correlations were detected between soil erodibility indices, and bulk density and sand content, while significant negative correlations were found between root mass density, silt, clay, and organic matter contents. CSEI increased linearly with inundation time and duration. The results are helpful for understanding the mechanism of seasonal fluctuations in soil erodibility for land frequently inundated by water.

The Dynamics of Tungsten in Soil: An Overview

The increasing use of tungsten in the production of green energy in the aerospace and military industries, and in many other hi-tech applications, may increase the content of this element in soil. This overview examines some aspects of the behavior of tungsten in soil, such as the importance of characteristics of soils in relation to bioavailability processes, the chemical approaches to evaluate tungsten mobility in the soil environment and the importance of adsorption and desorption processes. Tungsten behavior depends on soil properties of which the most important is soil pH, which determines the solubility and polymerization of tungstate ions and the characteristics of the adsorbing soil surfaces. During the adsorption and desorption of tungsten, iron, and aluminum oxides, and hydroxides play a key role as they are the most important adsorbing surfaces for tungsten. The behavior of tungsten compounds in the soil determines the transfer of this element in plants and therefore in the food chain. Despite the growing importance of tungsten in everyday life, environmental regulations concerning soil do not take this element into consideration. The purpose of this review is also to provide some basic information that could be useful when considering tungsten in environmental legislation.

White clover living mulch enhances soil health vs. annual cover crops

AbstractCover crops can improve soil health by increasing soil organic matter, soil porosity, permeability, and crop yield. Yet, land planted to cover crops are often limited by economic constraints. Perennial living mulch (LM) cover crops may provide better benefits to soil health because they are actively growing throughout the year and self‐regenerate without reseeding. The objective of this study was to compare the impact of a white clover (Trifolium repens L.) LM vs. annual cover crops on soil health traits. Treatments were established on a Cecil sandy loam soil in the fall of 2014 and annual cover crop treatments re‐established each fall of the following 3 yr. White clover re‐established in the LM without reseeding. Corn (Zea mays L.) was planted into the treatments in the spring of each year. Soils were sampled at the V4/V5, V12, and R5 stages of corn development and analyzed for chemical traits. Surface soil characteristics were measured after corn harvest in 2018. Soils in the LM system had lower lime buffering capacity and greater pH, base saturation, cation exchange capacity (CEC), Ca, K2O, Mg, P2O5, and total organic C concentrations than other treatments. Soil NH3 and NO3 had seasonal fluctuations associated with mineral N fertilizer and were lower in the LM treatment. After 3 yr, the soil bulk density was lower and porosity, water infiltration, and labile C were greater in surface soils from the LM treatment than in the surface soils of the other treatments. Use of a perennial LM cover crops expedited soil health regeneration compared to other treatments.