Soil Texture Classification Systems Research Articles

Transformation of soil texture schemes and determination of water-physical properties of soils

Measuring soil water-physical properties is laborious, time-consuming, and expensive. That provokes a lot of scientists to estimate them which action is troubled by the usage of different soil texture classification systems. The study proposes a rapid, reliable, and universally applicable methodology for soil textural transformations between different classification systems. The method of discrete mathematics is applied to make the conversion of particle-size classes from the Kachinsky system, which is used in Bulgaria to the International systems. Three different data sources were used to determine the water-physical properties of soils from textural data - 376 soil profiles from Bulgaria, extraction from the SoilGrids system for the Plovdiv district in Bulgaria and data from CanSIS/NSDB database. The relationship between the dependent variables field capacity (FC), wilting point (WP) and bulk density (BD), and independent variables sand, silt, and clay soil content was sought in the form of a regression equation. The applied stepwise regression procedure produces a close dependence between the soil texture and its water-physical properties.

Laser Diffraction as An Innovative Alternative to Standard Pipette Method for Determination of Soil Texture Classes in Central Europe

The paper presents the comparison of soil particle size distribution determined by standard pipette method and laser diffraction. Based on the obtained results (542 soil samples from 271 sites located in the Nitra, Váh and Hron River basins), regression models were calculated to convert the results of the particle size distribution by laser diffraction to pipette method. Considering one of the most common soil texture classification systems used in Slovakia (according to Novák), the emphasis was placed on the determination accuracy of particle size fraction <0.01 mm. Analysette22 MicroTec plus and Mastersizer2000 devices were used for laser diffraction. Polynomial regression model resulted in the best approximation of measurements by laser diffraction to values obtained by pipette method. In the case of particle size fraction <0.01 mm, the differences between the measured values by pipette method and both laser analyzers ranged in average from 3% up to 9% and from 2% up to 11% in the case of Analysette22 and Mastersizer2000, respectively. After correction, the differences decreased to average 3.28% (Analysette22) and 2.24% (Mastersizer2000) in comparison with pipette method. After recalculation of the data, laser diffraction can be used alongside the sedimentation methods.

Assessment of the Impacts of Land Use/Cover Change and Rainfall Change on Surface Runoff in China

Assessment of the impacts of land use/cover change (LUCC) and rainfall change on surface runoff depth can help provide an understanding of the temporal trend of variation of surface runoff and assist in urban construction planning. This study evaluated the impacts of LUCC and rainfall change on surface runoff depth by adopting the well-known Soil Conservation Service-Curve Number (SCS-CN) method and the widely used Long-Term Hydrologic Impact Assessment (L-THIA) model. National hydrologic soil group map of China was generated based on a conversion from soil texture classification system. The CN values were adjusted based on the land use/cover types and soil properties in China. The L-THIA model was configured by using the adjusted CN values and then applied nationally in China. Results show that nationwide rainfall changes and LUCC from 2005 to 2010 had little impact on the distribution of surface runoff, and the high values of runoff depth were mainly located in the middle and lower reaches of the Yangtze River. Nationally, the average annual runoff depths in 2005, 2010 and 2015 were 78 mm, 83 mm and 90 mm, respectively. For the 2015 land use data, rainfall change caused the variation of surface runoff depth ranging from −203 mm to 476 mm in different regions. LUCC from 2005 to 2015 did not cause obvious change of surface runoff depth, but expansion of developed land led to runoff depth increases ranging from 0 mm to 570 mm and 0 mm to 742 mm from 2005 to 2010 and 2010 to 2015, respectively. Potential solutions to urban land use change and surface runoff control were also analyzed.

Analysis of Agronomic Categories in Different Soil Texture Classification Systems

Different soil texture classification systems are used in Poland. The system most widely used in agriculture is named after Polish Soil Science Society (PSSS) and is described in the soil classification norm BN-78/9180-11 (BN1978 standard). The last edition of soil classification system and soil texture classes published by PSSS in 2008 (PSSS 2008 classification) is different from BN1978 standard. The aim of this paper is a quantitative and qualitative comparison of the compatibility of agronomic categories created according to the old textural classes (BN1978 standard) and the new textural classes (PSSS 2008 classification). The representative set of soil samples (n=316) for arable mineral soils in Poland were divided into agronomic categories according to these two soil classification systems. The agronomic categories, which comply with soil classification systems PSSS 1978 are widely used in agricultural advisory. The results of the study showed differences in the amount of soil samples classified for the corresponding agronomic category. The study also showed discrepancies in the fine particle (<0.02 mm) and colloidal fraction (<0.002 mm) content in the corresponding categories. The differences may affect the assessment of soil fertility in nutrients(abundance) such as potassium, magnesium and of soil liming needs, as well as appropriate determination of fertiliser doses.

Conversion between Soil Texture Classification Systems using the Random Forest Algorithm

This study focuses on the reclassification of a soil texture system following a hybrid approach in which the conventional particle-size distribution (PSD) models are coupled with a random forest (RF) algorithm for achieving more generally applicable and precise outputs. The existing parametric PSD models that could be used for this purpose have various limitations; different models frequently show unequal degrees of precision in different soils or under different environments. The authors present in this article a novel ensemble modeling approach in which the existing PSD models are used as ensemble members. An improvement in precision was proved by better statistical indicators for the results obtained, and the article documents that the ensemble model worked better than any of its constituents (different existing parametric PSD models). This study is verified by using a soil dataset from Slovakia, which was originally labeled by a national texture classification system, which was then transformed to the USDA soil classification system. However, the methodology proposed could be used more generally, and the information provided is also applicable when dealing with the soil texture classification systems used in other countries.

조명 및 카메라 이동속도가 토양 영상에 미치는 영향

Soil texture has an important influence on agriculture such as crop selection, movement of nutrient and water, soil electrical conductivity, and crop growth. Conventionally, soil texture has been determined in the laboratory using pipette and hydrometer methods requiring significant amount of time, labor, and cost. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture that needs more data than conventional agriculture. This paper is a part of overall research to develop an in-situ soil texture classification system using image processing. Issues investigated in this study were effects of sensor travel speed and light source and intensity on image quality. When travel speed of image sensor increased from 0 to 10 mm/s, travel distance and number of pixel were increased to 3.30 mm and 9.4, respectively. This travel distances were not negligible even at a speed of 2 mm/s (i.e., 0.66 mm and 1.4), and image degradation was significant. Tests for effects of illumination intensity showed that 7 to 11 Lux seemed a good condition minimizing shade and reflection. When soil water content increased, illumination intensity should be greater to compensate decrease in brightness. Results of the paper would be useful for construction, test, and application of the sensor.

Soil Texture Classification Algorithm Using RGB Characteristics of Soil Images

Soil texture has an important influence on agriculture, affecting crop selection, movement of nutrients and water, soil electrical conductivity, and crop growth. Soil texture has traditionally been determined in the laboratory using pipette and hydrometer methods that require a considerable amount of time, labor, and expense. Recently, in-situ soil texture classification systems using optical diffuse reflectometry or mechanical resistance have been reported, especially for precision agriculture where more data is needed than in conventional agriculture. This paper is a part of overall research to develop a soil texture classification system using image processing. Application of image processing was motivated by simple traditional approaches such as visual inspection and the “hand-feel” method. In this paper, the potential of soil texture classification using RGB histograms was investigated. Seven sites representing major Korean paddy soil series were selected, 4-6 core samples up to a 50-cm depth were obtained from each site, and each sample was segmented by 5-cm intervals. For each segmented soil sample, four surface images were taken using a miniaturized CCD camera, and texture fractions were determined by the pipette method. Scatter plots showed linear patterns between silt content and histogram variables such as brightness, skewness, and mode - brightness. When 5% averaged silt content was linearly regressed with mode - brightness, R2, RMSEC, and RMSEP were 0.96, 2.2%, and 6.3%, respectively. When soils were classified using USDA criteria, the laboratory method and the in-situ image processing method produced the same results for 48% of the samples.

Wavelet domain statistical hyperspectral soil texture classification

This communication presents an automatic soil texture classification system using hyperspectral soil signatures and wavelet-based statistical models. Previous soil texture classification systems are closely related to texture classification methods, where images are used for training and testing. In this study, we develop a novel system using hyperspectral soil textures, which provide rich information and intrinsic properties about soil textures, where two wavelet-domain statistical models, namely, the maximum-likelihood and hidden Markov models, are incorporated for the classification task. Experimental results show that these methods are both reliable and robust.

Particle‐Size Distributions

Conventional soil texture classification systems use different definitions of particle‐size distributions (PSDs). For example, sand in the International Soil Science Society (ISSS) system equals the combined separate limits of coarse silt and sand in the USDA system. Because relationships between texture and other soil properties are affected by these differences, the ability to merge survey data in environmental studies is limited. Previous research calculated two PSD statistics, namely the geometric mean particle diameter (dg) and its standard deviation (σg), which do not depend on separate limits. We expanded the development of the PSD statistics dg and σg to compare the USDA and ISSS systems, develop relationships with soil properties, include rock fragments, and simplify the USDA texture classification to facilitate the use of soil survey data in environmental research. We found that (i) for equal clay and sand fractions, the texture of a soil sample as described by the USDA system has larger dg and σg values than in ISSS; (ii) for equal clay and sand fractions, soil samples have larger values of cation‐exchange capacity (CEC) in the ISSS than in the USDA system; (iii) small differences between some of the traditional 12 USDA classes are reflected in the dg and σg values for samples containing rocks, thereby presenting a rationale for simplification; and (iv) with this rationale, the 12 USDA classes were aggregated into five classes.