Search

A thorough quantification of soil chemical properties is essential for assessing the engineering properties of forest soils for road design, construction, and maintenance. Here, we investigate the applicability of visible–near-infrared (Vis–NIR) spectroscopy in conjunction with advanced statistical analysis for estimation of soil chemical properties. Sixty forest soil samples were collected and analyzed for pH, electrical conductivity (EC), CaCO3, organic matter (OM), and cation exchange capacity (CEC) with established laboratory methods. The spectral measurements were performed with a Vis–NIR spectrometer within a range of 350–2,500 nm. To estimate abovementioned soil properties from reflectance spectra, advanced statistical techniques including partial least squares regression (PLSR), hybrid partial least squares and artificial neural networks (PLS–DI–ANN) models, hybrid partial least squares and adaptive neural fuzzy inference system (PLS–DI–ANFIS) models, as well as narrow band spectral indices were applied. The obtained results ​​indicate that the PLS–DI–ANFIS models show great potential for the estimation of pH, EC, OM, and CEC from reflectance spectra and their first derivatives, exhibiting higher R2 values and lower RMSE than the other investigated models. The estimation accuracy for CaCO3, however, was low for all applied methods. The results confirm that Vis–NIR spectroscopy may be applied as a rapid and cost-efficient alternative to standard chemical soil analysis techniques, aiding forest road design, construction, and maintenance.

Saline playas in arid and semiarid regions of the world are significant sources of unconsolidated sediments susceptible to aeolian transport. Lake Urmia in Iran, one of the largest saltwater lakes on Earth, has waned to approximately 18% of its original size due to groundwater pumping and surface water diversions. This has led to ecosystem degradation, accelerated desertification and frequent dust storms, causing public health problems. In this paper we introduce a new framework for delineation of dust source zones and estimation of dust occurrence probability based on remotely sensed land surface properties (soil moisture and vegetation cover), soil texture, wind speed, and measured dust frequencies. Observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the Urmia Lake basin were utilized to determine the Normalized Difference Vegetation Index (NDVI) and to estimate surface soil moisture with a recently introduced optical trapezoid model. The soil textures extracted from the SoilGrids database and wind speeds obtained from local weather stations together with the estimated surface soil moisture were found to be highly correlated with dust emission probability. When the surface soil moisture is low, wind speed is the major determinant for dust occurrence. With increasing surface soil moisture, the dust occurrence probability decreases. Soil moisture effects are more pronounced at high wind speeds. While at low wind speeds the dust occurrence probabilities for the investigated soil textures loam, clay loam and sandy clay loam are similar, at higher wind speeds the sandy clay loam texture exhibits the highest susceptibility to dust generation.

Near-surface soil moisture is a spatially highly heterogeneous state variable that affects the hydrologic partitioning of precipitation, net ecosystem exchange, and land–atmosphere interactions. In concert with the rapid advancement of large-scale hyperspectral remote sensing technology over the last decade, numerous methods relating reflectance to near-surface soil moisture have been proposed. Although there is evidence that surface reflectance is conjointly controlled by moisture and salt content, little is known about the impact of soil salinity. To study the effects of soil salinity, near infrared to short-wave infrared reflectance spectra were measured in a controlled laboratory environment for samples representing a wide range of salinity levels. A previously proposed surface moisture prediction method based on geometrical attributes of an inverted Gaussian (IG) function fitted to hyperspectral reflectance curves was revisited. Improvements were proposed by first modifying the geometrical attributes of the IG applied to predict near-surface soil moisture and by concurrently considering multiple geometrical attributes of the IG function as input for trained artificial neural networks (ANNs). Although previously applied linear regression models that relate a single geometrical parameter to soil moisture failed to satisfactorily predict independently measured surface soil moisture, considerable improvements in prediction accuracy were achieved with both geometrical attribute modification and ANN simulations.

The impact of biodiversity loss on soil functions is well established via laboratory experiments that generally consider soil biota groups in isolation from each other, a condition rarely present in field soils. As a result, our knowledge about anthropogenic-induced changes in biodiversity and associated soil functions is limited. We quantified an array of soil biological constituents (plants, earthworms, nematodes, bacteria, and fungi) to explore their interactions and to characterize their influence on various soil functions (habitat for soil organisms, air and water regulation, and recycling of nutrients and organic waste) along a legacy Cu pollution gradient. Increasing Cu concentrations had a detrimental impact on both plant growth and species richness. Belowground soil biota showed similar responses, with their sensitivity to elevated Cu concentrations decreasing in the order: earthworms > bacteria > nematodes > fungi. The observed loss of soil biota adversely affected natural soil bioturbation, aggregate formation and stabilization, and decomposition and mineralization processes and therefore resulted in compacted soil with narrow pore size distributions and overall smaller pores, restricted air and water storage and flow, and impeded C, N, and P cycling. The simultaneous evolution of soil biodiversity and functions along the Cu gradient emphasized the key role of soil life in controlling ecosystem services. Furthermore, results indicated that different soil biodiversity and functional indicators started to decline (10% loss) within a Cu concentration range of 110 to 800 mg total Cu kg-1. © Soil Science Society of America, 5585 Guilford Rd.

The ecosystem services approach endeavors to incorporate the economic value of ecosystems into decision making. This is because many natural resources are subject to market failure. As a result, many economic decisions omit the impact that natural resource use has on the earth’s resources and the life support system it provides. Hence, one of the objectives of the ecosystem services approach is to employ economic valuation of natural resources in micro- and macroeconomic policy design, implementation, and evaluation. In this article we examine valuation concepts, and ask why we might attempt to economically value the contribution of soils to the provision of ecosystem services. We go on to examine economic valuation methods and review economic valuation of soils. By surveying prices of soils on the web we are able to make a first, limited global assessment of direct market value of topsoil prices. We then consider other research efforts to value soil. Finally, we consider how the valuation of soil can meaningfully be used in the introduction of improved resource management mechanisms such as decision support tools on which valuation can be based, within the UN’s System of Environmental and Economic Accounts (SEEA) and policy mechanisms like Payments for Ecosystem Services (PES).

The characterization and description of important soil processes such as water vapor transport, volatilization of pesticides, and hysteresis require accurate means for measuring the soil water characteristic (SWC) at low water potentials. Until recently, measurement of the SWC at low water potentials was constrained by hydraulic decoupling and long equilibration times when pressure plates or single-point, chilled-mirror instruments were used. A new, fully automated vapor sorption analyzer (VSA) helps to overcome these challenges and allows faster measurement of highly detailed water vapor sorption isotherms. In this technical note we present a comprehensive evaluation of the VSA instrument for a wide range of differently textured soils and discuss optimal measurement settings. The effects of operation mode, air-flow rate, sample pretreatment, test temperature, sample mass, and mass trigger point on resultant sorption isotherms were evaluated for a relative humidity (RH) range from 0.10 to 0.90. Both adsorption and desorption branches were measured for all soils within a reasonable time period (10-50 h). Sample masses larger than 3.5 g resulted in incomplete adsorption and desorption, while oven-dry (105°C) samples of coarse-textured soils exhibited water repellency characteristics. The required measurement times were strongly correlated with clay content and influenced by high organic carbon content.

Modeling water distribution and flow in partially saturated soils requires knowledge of the soil water characteristic (SWC). However, measurement of the SWC is challenging and time-consuming and, in some cases, not feasible. This study introduces two predictive models (FW–model and AW–model) for the SWC, derived from readily available soil properties such as texture and bulk density. A total of 46 undisturbed soils from different horizons at 15 locations across Denmark were used for model evaluation. The FW–model predicts the volumetric water content as a function of volumetric fines content (organic matter and clay). It performed reasonably well for the dry-end of SWC (above a pF value of 2.0; pF = log(|ψ|), where ψ is the matric potential in cm), but did not do as well closer to saturated conditions. The AW–model predicts the volumetric water content as a function of volumetric content of different particle size fractions (organic matter, clay, silt, and fine and coarse sands). The volumetric content of a particular soil particle size fraction was considered if it contributed to the pore size fraction still occupied with water at the given pF value. Hereby, the AW–model implicitly assumes that a given particle size fraction creates an analogue pore size fraction and further this pore size fraction filled with water is corresponding to a certain pF value according to the well-known capillary rise equation. The AW–model was found to be quite robust, and it performed exceptionally well for pF values ranging from 0.4 to 4.2 for different soil types. For prediction of the continuous SWC, it is recommended to parameterize the van Genuchten model based on the SWC data points predicted by the AW–model. Modelling water distribution and flow in partially saturated soils requires knowledge of the soil-water characteristic (SWC). However, measurement of the SWC is challenging and time-consuming, and in some cases not feasible. This study introduces two predictive models (Xw-model and Xw*-model) for the SWC, derived from readily available soil properties such as texture and bulk density. A total of 46 soils from different horizons at 15 locations across Denmark were used for models evaluation. The Xw-model predicts the volumetric water content as a function of volumetric fines content (organic matter and clay). It performed reasonably well for the dry-end (above a pF value of 2.0; pF = log(|Ψ|), where Ψ is the matric potential in cm), but did not do as well closer to saturated conditions. The Xw*-model gives the volumetric water content as a function of volumetric content of particle size fractions (organic matter, clay, silt, fine and coarse sand), variably included in the model depending on the pF value. The volumetric content of a particular soil particle size fraction was included in the model if it was assumed to contribute to the pore size fraction still occupied with water at the given pF value. Hereby, the Xw*-model implicitly assumes that a given particle size fraction creates an analogue pore size fraction and, also, is based on the validity of the well-known capillary law equation relating equivalent drained pore size to the soil-water matric potential. The Xw*-model was found to be quite robust, and it performed exceptionally well for all tested pF values ranging from 0.4 to 4.2. For prediction of the continuous SWC, it is recommended that the van Genuchten model is parameterized based on the SWC data points predicted by the Xw*-model.

Soil texture and the soil water characteristic are key properties used to estimate flow and transport parameters. Determination of clay content is therefore critical for understanding of plot-scale soil heterogeneity. With increasing interest in proximal soil sensing, there is the need to relate obtained signals to soil properties of interest. Inference of soil texture, especially clay mineral content, from instrument response from electromagnetic induction and radiometric methods is of substantial interest. However, the cost of soil sampling and analysis required to link proximal measurements and soil properties, for example, clay mineral content, can sometimes outweigh the benefits of using a fast proximal technique. In this paper, we propose that determination of a soil's hygroscopic water content at 50% atmospheric relative humidity (RH50), which is time and cost efficient, and particularly suitable for developing countries, can act as a useful surrogate for clay content in interpreting soil spatial patterns based on proximal signals. We used standard clays such as kaolinite, illite, and montmorillonite to determine the water release characteristic as a function of hygroscopic water content. We also determined clay content of soils from temperate (Arizona, United States) and tropical (Trinidad) regions using the hydrometer method and hygroscopic water content for soils equilibrated at RH50. We found linear dependence of clay percentage and RH50 for a range of soil mineralogies. Hygroscopic water measurements offer an inexpensive and simple way to estimate site-specific clay mineral content that in turn can be used to interpret geophysical signal data in reconnaissance surveys.

Plant growth in restricted volumes of porous material is of interest for advanced life support systems for the National Aeronautics and Space Administration's future space missions. Reduced gravity conditions may affect fluid behavior in partially saturated porous media, requiring special considerations for growth media selection and root module design to ensure reliable water, air, and nutrient supply. Evidence suggests that fluid displacement patterns become unstable and enhance phase entrapment in the absence of gravity, thereby modifying macroscopic transport properties essential for fluid management decisions. Parabolic flight experiments have shown that preferential flows may lead to phase (air or gas) entrapment that would affect gaseous diffusion, as illustrated by lattice Boltzmann simulations. In microgravity, unstable flow patterns and particle rearrangement introduce uncertainty associated with particulate root growth media. These findings suggest that future efforts toward designing porous media and plant root modules in reduced gravity should focus on engineered plant growth media with stable pore space and spatially segregated domains that support water and nutrient retention in addition to gas exchange.