Predicting soil particle density from clay and soil organic matter contents

Schjønning, Per; McBride, R. A.; Keller, Thomas; Obour, Peter Bilson

doi:10.1016/j.geoderma.2016.10.020

Cited by 93 publications

(54 citation statements)

References 12 publications

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“…Results were analyzed with GRADISTATv8 [55] to determine the sorting and sample distribution, and using the soil texture package [56] in the open source software R (version 3.5.0) [57]. To generate soil texture triangles and classify the samples according to the USDA soil texture system [58], results were converted into weight percentages (wt%) using particle density values by Schjønning et al [59].…”

Section: Grain-size Distributionmentioning

confidence: 99%

Sedimentary and geochemical characteristics of two small permafrost-dominated Arctic river deltas in northern Alaska

Fuchs

Grosse

Jones

et al. 2018

Arktos

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Arctic river deltas are highly dynamic environments in the northern circumpolar permafrost region that are affected by fluvial, coastal, and permafrost-thaw processes. They are characterized by thick sediment deposits containing large but poorly constrained amounts of frozen organic carbon and nitrogen. This study presents new data on soil organic carbon and nitrogen storage as well as accumulation rates from the Ikpikpuk and Fish Creek river deltas, two small, permafrost-dominated Arctic river deltas on the Arctic Coastal Plain of northern Alaska. A soil organic carbon storage of 42.4 ± 1.6 and 37.9 ± 3.5 kg C m − 2 and soil nitrogen storage of 2.1 ± 0.1 and 2.0 ± 0.2 kg N m − 2 was found for the first 2 m of soil for the Ikpikpuk and Fish Creek river delta, respectively. While the upper meter of soil contains 3.57 Tg C, substantial amounts of carbon (3.09 Tg C or 46%) are also stored within the second meter of soil (100-200 cm) in the two deltas. An increasing and inhomogeneous distribution of C with depth is indicative of the dominance of deltaic depositional rather than soil forming processes for soil organic carbon storage. Largely, mid-to late Holocene radiocarbon dates in our cores suggest different carbon accumulation rates for the two deltas for the last 2000 years. Rates up to 28 g C m − 2 year − 1 for the Ikpikpuk river delta are about twice as high as for the Fish Creek river delta. With this study, we highlight the importance of including these highly dynamic permafrost environments in future permafrost carbon estimations.

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Section: Grain-size Distributionmentioning

confidence: 99%

Sedimentary and geochemical characteristics of two small permafrost-dominated Arctic river deltas in northern Alaska

Fuchs

Grosse

Jones

et al. 2018

Arktos

View full text Add to dashboard Cite

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“…The solid permittivity of soil and sand were obtained from [37], while that of gravel was obtained from [43]. In addition, a specific density of 2.65 g cm −3 [44] was adopted for all three materials. Finally, both the sand and gravel were dry samples while the water content of the soil was measured using the oven drying method.…”

Section: Methodsmentioning

confidence: 99%

Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

2019

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This study reports on the combination of data from a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive depth estimation of buried radioactive sources. The use of the GPR was to enable the estimation of the material density required for the calculation of the depth of the source from the radiation data. Four different models for bulk density estimation were analysed using three materials, namely: sand, gravel and soil. The results showed that the GPR was able to estimate the bulk density of the three materials with an average error of 4.5%. The density estimates were then used together with gamma ray measurements to successfully estimate the depth of a 658 kBq ceasium-137 radioactive source buried in each of the three materials investigated. However, a linear correction factor needs to be applied to the depth estimates due to the deviation of the estimated depth from the measured depth as the depth increases. This new application of GPR will further extend the possible fields of application of this ubiquitous geophysical tool.

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“…Generally, absorbance spectra are employed in spectral analysis because unlike inversion (1/R), root mean square ( ), logarithm (lg R), and other forms, they have has practical spectral meaning [ 33 ]. For better modeling results and improved nonlinear relations, the previously pretreated spectral reflectance was transformed into absorbance.…”

Section: Methodsmentioning

confidence: 99%

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

et al. 2017

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Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance ( = 0.907, RMSEC = 0.425%, = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance ( = 0.888, RMSEC = 0.446%, = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area.

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Predicting soil particle density from clay and soil organic matter contents

Cited by 93 publications

References 12 publications

Sedimentary and geochemical characteristics of two small permafrost-dominated Arctic river deltas in northern Alaska

Sedimentary and geochemical characteristics of two small permafrost-dominated Arctic river deltas in northern Alaska

Nonintrusive Depth Estimation of Buried Radioactive Wastes Using Ground Penetrating Radar and a Gamma Ray Detector

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

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