Evaluation of Laser Diffraction Analysis of Particle Size Distribution of Typical Soils in China and Comparison With the Sieve-Pipette Method

Wang, Weipeng; Liu, Jianli; Zhang, Jiabao; Li, Xiaopeng; Cheng, Yi; WenWen, Xin; Yan, Yingkun

doi:10.1097/ss.0b013e31829908be

Cited by 23 publications

(17 citation statements)

References 2 publications

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“…This result was well supported by the report that a laser particle-size analysis underestimated the clay content relative to the classical sedimentation method, and this underestimation increased with increasing clay content (Loizeau et al, 1994). Our exponential fitting between the two methods for the clay fractions, however, disagreed with previously reported linear fittings (Konert and Vandenberghe, 1997;Beuselinck et al, 1998;Eshel et al, 2004;Yang et al, 2009;Wang et al, 2013a). This discrepancy may have been caused by differences in pre-treatment, dispersion, optical theory, optical parameters, or particle mineralogy or morphology (Özer et al, 2010).…”

Section: Relationships Between Laser-diffraction Methods and Sieve-pipsupporting

confidence: 89%

“…The contents of the sand fractions were higher for LDM than SPM in 100 of the 235 samples. These results indicated that LDM underestimated clay content relative to SPM by an average of 45.1% but overestimated silt content by an average of 18.3% in the soils of the Loess Plateau, consistent with other findings for diverse soil textures (Loizeau et al, 1994;Konert and Vandenberghe, 1997;Eshel et al, 2004;Shein et al, 2006;Yang et al, 2009;Wang et al, 2013a). Beuselinck et al (1998), however, reported that LDM underestimated the clay fraction of silty soils but generally overestimated the clay fraction of milled quartz samples, and Wu et al (1993) and Muggler et al (1997) reported agreement between the two methods.…”

Section: Comparison Of Particle-size Distributions Between Laser-diffsupporting

confidence: 87%

“…3. The densities of particles of different sizes are commonly assumed to be a constant (2.65 Mg m -3 ), which is not true for soil and which has been reported to vary from 1.66 to 2.99 Mg m -3 (Vdović et al, 2010;Miller and Schaetzl, 2012;Wang et al, 2013a). 4.…”

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confidence: 99%

“…More importantly, LDM provides a continuous PSD curve rather than an arbitrary division of particle sizes, as in the traditional SPM. This advantage alone is valuable for more detailed data analyses and transformations among the various classification systems (Wang et al, 2013a). However, the notable limitation of the LDM measurement is the under-reporting of clay content and the dependence on well-described optical properties of refractive index and absorption, and also the high instrument's cost and lack of standard operation in soil analysis (Campbell, 2003;Makó et al, 2014).…”

mentioning

confidence: 99%

“…We thus cannot directly determine the texture of a soil by using PSD data derived by LDM (volume, %), because the current classification systems of soil texture (e.g., USDA) are typically based on PSD data derived by SPM (mass, %). Various studies have thus compared PSDs between the two techniques and have attempted to produce models for converting LDM PSD data to SPM data (Konert and Vandenberghe, 1997;Eshel et al, 2004;Wang et al, 2013a). These calibration relationships enable researchers to compare laser data with the large amount of literature available on sediment texture based on settling technique (Ramaswamy and Rao, 2006).…”

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confidence: 99%

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Determination of Soil Texture by Laser Diffraction Method

Yang

Zhang

et al. 2015

Soil Science Society of America Journal

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The laser-diffraction method (LDM) can rapidly determine soil particlesize distributions (PsDs), but LDM-derived PsDs cannot be directly used to classify soil textures by referring to the standards of the classical sievepipette method (sPM). Our objectives were to explore calibration models for converting PsD data from LDM (volume, %) to sPM (mass, %), and to evaluate the precision of textural classification by using LDM data. we determined the PsDs using both methods for 235 soil samples of various textures collected from three typical land uses, on the Loess Plateau of China. The LDM generally underestimated clay fractions by an average of 45.1%, and overestimated silt fractions by an average of 18.3% compared with sPM. Differences in PsD data between the two methods, indicated by coefficient Cs, increased with increasing clay contents for the 235 samples (P < 0.05). Three calibration models could, however, convert the clay, silt, and sand contents from the volume percentage (LDM) to the mass percentage (sPM). After the conversion, the mean coefficient C between the two methods decreased from 7.9 to 4.1% for the validation samples (n = 78). The distributions of soil textures within the usDA textural triangle agreed well in 71 of the 78 samples, for measured and converted PsD data. The three types of land use did not affect the differences between measured and converted PsD data (P > 0.05). soil textures can thus be rapidly determined by converting PsD data from the faster volume-based LDM to data equivalent to the mass-based sPM, independent of land-use type.

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Section: Relationships Between Laser-diffraction Methods and Sieve-pipsupporting

confidence: 89%

Section: Comparison Of Particle-size Distributions Between Laser-diffsupporting

confidence: 87%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Determination of Soil Texture by Laser Diffraction Method

Yang

Zhang

et al. 2015

Soil Science Society of America Journal

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An approach for broad‐scale predictive soil properties mapping in low‐relief areas based on responses to solar radiation

Liu

Rossiter

Song

et al. 2020

Soil Science Soc of Amer J

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In low‐relief areas, easily observed landscape features such as terrain and vegetation often do not spatially co‐vary with soil conditions to the level that they can be effectively used in predictive soil mapping. This paper proposes an approach to predicting soil spatial variation over such areas at a coarse resolution by constructing environmental covariates from the dynamic feedbacks of land surface in response to solar radiation. These feedbacks are captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations as a time series of four temperatures per day (1:30 AM, 10:30 AM, 1:30 PM, 10:30 PM) converted into five covariates representing the features of the time series. The approach is demonstrated by mapping sand, silt, soil organic matter (SOM), and pH over a 12,000‐km2 low‐relief area of Jiangsu Province, China, using a random forest model trained on 144 soil observations. The model was built and evaluated for 13 single days. For the non‐winter days with relatively high solar radiation, the covariates were strongly correlated with the soil properties, so that their use in predictive mapping gives good results for soil texture and useful results for SOM and pH. We also present plausible physical interpretations for the action of the covariates based on thermal properties of soils. We conclude that, despite the coarse resolution of the MODIS‐derived covariates, the proposed approach is a feasible solution for predictive soil mapping over large areas of low relief and should be investigated at finer resolutions.

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Allocation of photosynthestically-fixed carbon in plant and soil during growth of reed (Phragmites australis) in two saline soils

Qiu²,

Chen

et al. 2016

Plant Soil

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Evaluation of Laser Diffraction Analysis of Particle Size Distribution of Typical Soils in China and Comparison With the Sieve-Pipette Method

Cited by 23 publications

References 2 publications

Determination of Soil Texture by Laser Diffraction Method

Determination of Soil Texture by Laser Diffraction Method

An approach for broad‐scale predictive soil properties mapping in low‐relief areas based on responses to solar radiation

Allocation of photosynthestically-fixed carbon in plant and soil during growth of reed (Phragmites australis) in two saline soils

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