“Single Value” Soil Properties: A Study of the Significance of Certain Soil Constants. V. On the Changes Produced in a Soil by Oven Drying (With One Text-figure.)

1. It is shown that results for the loss in weight of a soil on oven heating can be obtained to a very satisfactory degree of accuracy when a Hearson electrically controlled oven is used.2. Results obtained by heating soils to temperatures ranging from 5° to 250° give smooth curves connecting loss in weight with rise in temperature; from which it is concluded that there is no sudden alteration in the structure of a soil when it is heated to 100°, and that the airdry moisture of a soil, as determined with sufficient accuracy by the usual methods, is a convenient empirical factor, but not a representation of any fundamental soil property.3. An examination is made of the factors contributing to the observed total loss in weight when the soil is heated, and an explanation offered of the contributions made by the different types of soil water and by the soil colloids. It is found that the conclusions drawn from this discussion confirm views developed earlier with regard to the behaviour of the water in the soil, and the absence of any sharp dividing line between the different classes into which the soil water is usually divided.

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Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

Scientific Research Journal ◽

10.24191/srj.v11i1.5416 ◽

2014 ◽

Vol 11 (1) ◽

pp. 15

Author(s):

Set Foong Ng ◽

Pei Eng Ch’ng ◽

Yee Ming Chew ◽

Kok Shien Ng

Keyword(s):

Soil Properties ◽

Lagrange Multipliers ◽

Unbiased Estimator ◽

Soil Property ◽

Minimum Variance ◽

Distance Method ◽

True Value ◽

A Site ◽

Method Of Lagrange Multipliers ◽

Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

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Research trends of microplastics in the soil environment: Comprehensive screening of effects

Soil Ecology Letters ◽

10.1007/s42832-021-0077-3 ◽

2021 ◽

Author(s):

Shin Woong Kim ◽

Matthias C. Rillig

Keyword(s):

Soil Properties ◽

Additional Data ◽

Soil Property ◽

Dry Weight ◽

Current Data ◽

Soil Biota ◽

Soil Parameters ◽

Soil Environment ◽

Soil System ◽

Sensitivity Distribution

AbstractWe collated and synthesized previous studies that reported the impacts of microplastics on soil parameters. The data were classified and integrated to screen for the proportion of significant effects, then we suggest several directions to alleviate the current data limitation in future experiments. We compiled 106 datasets capturing significant effects, which were analyzed in detail. We found that polyethylene and pellets (or powders) were the most frequently used microplastic composition and shape for soil experiments. The significant effects mainly occurred in broad size ranges (0.1–1 mm) at test concentrations of 0.1%–10% based on soil dry weight. Polyvinyl chloride and film induced significant effects at lower concentrations compared to other compositions and shapes, respectively. We adopted a species sensitivity distribution (SSD) and soil property effect distribution (SPED) method using available data from soil biota, and for soil properties and enzymes deemed relevant for microplastic management. The predicted-no-effect-concentration (PNEC)-like values needed to protect 95% of soil biota and soil properties was estimated to be between 520 and 655 mg kg−1. This study was the first to screen microplastic levels with a view toward protecting the soil system. Our results should be regularly updated (e.g., quarterly) with additional data as they become available.

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A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Land ◽

10.3390/land10050544 ◽

2021 ◽

Vol 10 (5) ◽

pp. 544

Author(s):

Jetse J. Stoorvogel ◽

Vera L. Mulder

Keyword(s):

Comparative Analysis ◽

Soil Properties ◽

Soil Property ◽

Evaluation Criteria ◽

Clay Content ◽

Soil Database ◽

Global Environmental ◽

Global Soil ◽

Mean Square Difference ◽

Methodological Evaluation

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

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Using Multivariate Geostatistics to Assess Patterns of Spatial Dependence of Apparent Soil Electrical Conductivity and Selected Soil Properties

The Scientific World JOURNAL ◽

10.1155/2014/712403 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Glécio Machado Siqueira ◽

Jorge Dafonte Dafonte ◽

Montserrat Valcárcel Armesto ◽

Ênio Farias França e Silva

Keyword(s):

Electrical Conductivity ◽

Soil Properties ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Clay Content ◽

Data Sets ◽

Soil Electrical Conductivity ◽

Multivariate Geostatistics ◽

Northwestern Spain

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECadata sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECaand gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECaand clay content (ranging from 0.197 to 0.495, when different ECarecording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECadata sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECaas secondary variable with respect to the use of ordinary kriging.

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Profile Soil Property Estimation Using a VIS-NIR-EC-Force Probe

Transactions of the ASABE ◽

10.13031/trans.12049 ◽

2017 ◽

Vol 60 (3) ◽

pp. 683-692 ◽

Cited By ~ 5

Author(s):

Yongjin Cho ◽

Kenneth A. Sudduth ◽

Scott T. Drummond

Keyword(s):

Soil Properties ◽

Water Content ◽

Bulk Density ◽

Soil Property ◽

Soil Strength ◽

Reflectance Spectra ◽

Land Resource ◽

Strength Data ◽

Soil Sensors ◽

Combining Data

Abstract. Combining data collected in-field from multiple soil sensors has the potential to improve the efficiency and accuracy of soil property estimates. Optical diffuse reflectance spectroscopy (DRS) has been used to estimate many important soil properties, such as soil carbon, water content, and texture. Other common soil sensors include penetrometers that measure soil strength and apparent electrical conductivity (ECa) sensors. Previous field research has related these sensor measurements to soil properties such as bulk density, water content, and texture. A commercial instrument that can simultaneously collect reflectance spectra, ECa, and soil strength data is now available. The objective of this research was to relate laboratory-measured soil properties, including bulk density (BD), total organic carbon (TOC), water content (WC), and texture fractions to sensor data from this instrument. At four field sites in mid-Missouri, profile sensor measurements were obtained to 0.9 m depth, followed by collection of soil cores at each site for laboratory measurements. Using only DRS data, BD, TOC, and WC were not well-estimated (R2 = 0.32, 0.67, and 0.40, respectively). Adding ECa and soil strength data provided only a slight improvement in WC estimation (R2 = 0.47) and little to no improvement in BD and TOC estimation. When data were analyzed separately by major land resource area (MLRA), fusion of data from all sensors improved soil texture fraction estimates. The largest improvement compared to reflectance alone was for MLRA 115B, where estimation errors for the various soil properties were reduced by approximately 14% to 26%. This study showed promise for in-field sensor measurement of some soil properties. Additional field data collection and model development are needed for those soil properties for which a combination of data from multiple sensors is required. Keywords: NIR spectroscopy, Precision agriculture, Reflectance spectra, Soil properties, Soil sensing.

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Method for Quick Prediction of Hydraulic Conductivity and Soil-Water Retention of Unsaturated Soils

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118798486 ◽

2018 ◽

Vol 2672 (52) ◽

pp. 108-117 ◽

Cited By ~ 1

Author(s):

Shaoyang Dong ◽

Yuan Guo ◽

Xiong (Bill) Yu

Keyword(s):

Finite Element ◽

Hydraulic Conductivity ◽

Soil Properties ◽

Soil Water ◽

Unsaturated Soil ◽

Unsaturated Soils ◽

Hydraulic Properties ◽

Water Retention ◽

Characteristic Curve ◽

Soil Water Retention

Hydraulic conductivity and soil-water retention are two critical soil properties describing the fluid flow in unsaturated soils. Existing experimental procedures tend to be time consuming and labor intensive. This paper describes a heuristic approach that combines a limited number of experimental measurements with a computational model with random finite element to significantly accelerate the process. A microstructure-based model is established to describe unsaturated soils with distribution of phases based on their respective volumetric contents. The model is converted into a finite element model, in which the intrinsic hydraulic properties of each phase (soil particle, water, and air) are applied based on the microscopic structures. The bulk hydraulic properties are then determined based on discharge rate using Darcy’s law. The intrinsic permeability of each phase of soil is first calibrated from soil measured under dry and saturated conditions, which is then used to predict the hydraulic conductivities at different extents of saturation. The results match the experimental data closely. Mualem’s equation is applied to fit the pore size parameter based on the hydraulic conductivity. From these, the soil-water characteristic curve is predicted from van Genuchten’s equation. The simulation results are compared with the experimental results from documented studies, and excellent agreements were observed. Overall, this study provides a new modeling-based approach to predict the hydraulic conductivity function and soil-water characteristic curve of unsaturated soils based on measurement at complete dry or completely saturated conditions. An efficient way to measure these critical unsaturated soil properties will be of benefit in introducing unsaturated soil mechanics into engineering practice.

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Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

Remote Sensing ◽

10.3390/rs12081242 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1242 ◽

Cited By ~ 2

Author(s):

Sumanta Chatterjee ◽

Jingyi Huang ◽

Alfred E. Hartemink

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Soil Properties ◽

Soil Water ◽

Surface Soil ◽

Reference Network ◽

Surface Soil Moisture ◽

Ancillary Data ◽

Terrain Parameters ◽

The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

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