Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions

Geoderma ◽  
2020 ◽  
Vol 365 ◽  
pp. 114214
Author(s):  
Zong Wang ◽  
Wenjiao Shi ◽  
Wei Zhou ◽  
Xiaoyan Li ◽  
Tianxiang Yue
Keyword(s):  
Machine Learning ◽  
Particle Size ◽  
Soil Particle ◽  
Regression Kriging ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Soil Particle Size ◽  
Soil Particle Size Fractions ◽  
Log Ratio

scholarly journals Compositional balance should be considered in the mapping of soil particle-size fractions using hybrid interpolators

2021 ◽  
Author(s):  
Mo Zhang ◽  
Wenjiao Shi
Keyword(s):  
Particle Size ◽  
Compositional Data ◽  
Regional Scale ◽  
Soil Particle ◽  
Heihe River ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Soil Particle Size ◽  
Soil Particle Size Fractions ◽  
Log Ratio

Abstract. Digital soil mapping of soil particle-size fractions (PSFs) using log-ratio methods is a widely used technique. As a hybrid interpolator, regression kriging (RK) provides a way to improve prediction accuracy. However, there have been few comparisons with other techniques when RK is applied for compositional data, and it is not known if its performance based on different balances of isometric log-ratio (ILR) transformation is robust. Here, we compared the generalized linear model (GLM), random forest (RF), and their hybrid patterns (RK) using different transformed data based on three ILR balances, with 29 environmental covariables (ECs) for the prediction of soil PSFs in the upper reaches of the Heihe River Basin (HRB), China. The results showed that the RF performed best, with more accurate predictions, but the GLM produced a more unbiased prediction. As a hybrid interpolator, RK was recommended because it widened the data ranges of the prediction values, and modified the bias and accuracy of most models, especially the RF. The prediction maps generated from RK revealed more details of the soil sampling points than the other models. Different data distributions were produced for the three ILR balances. Using the most abundant component of the compositional data as the first component of the permutations was not considered to be the right choice because it produced the worst performance. Based on the relative abundance of the components, we recommend that the focus should be on data distribution. This study provides a reference for the mapping of soil PSFs combined with transformed data at the regional scale.

scholarly journals Compositional balance should be considered in soil particle-size fractions mapping using hybrid interpolators

2020 ◽  
Author(s):  
Mo Zhang ◽  
Wenjiao Shi
Keyword(s):  
Particle Size ◽  
Compositional Data ◽  
Regional Scale ◽  
Soil Particle ◽  
Heihe River ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Soil Particle Size ◽  
Soil Particle Size Fractions ◽  
Log Ratio

Abstract. Digital soil mapping of soil particle-size fractions (PSFs) using log-ratio methods has been widely used. As a hybrid interpolator, regression kriging (RK) is an alternative way to improve prediction accuracy. However, there is still a lack of systematic comparison and recommendation when RK was applied for compositional data. Whether performance based on different balances of isometric log-ratio (ILR) transformation is robust. Here, we systematically compared the generalized linear model (GLM), random forest (RF), and their hybrid pattern (RK) using different balances of ILR transformed data of soil PSFs with 29 environmental covariables for prediction of soil PSFs on the upper reaches of the Heihe River Basin. The results showed that RF had better performance with more accurate predictions, but GLM had a more unbiased prediction. For the hybrid interpolators, RK was recommended because it widened data ranges of the prediction results, and modified bias and accuracy for most models, especially for RF. The drawback, however, existed due to the data distributions and model algorithms. Moreover, prediction maps generated from RK demonstrated more details of soil sampling points. Three ILR transformed data based on sequential binary partitions (SBP) made different distributions, and it is not recommended to use the most abundant component of compositions as the first component of permutations. This study can reference spatial simulation of soil PSFs combined with environmental covariables and transformed data at a regional scale.

SOIL PARTICLE SIZE FRACTIONS DETERMINED BY GAMMA-RAY ATTENUATION

Soil Science ◽  
1999 ◽  
Vol 164 (6) ◽  
pp. 403-410 ◽  
Author(s):  
Carlos Manoel Pedro Vaz ◽  
João de Mendonça Naime ◽  
Álvaro Macedo
Keyword(s):  
Particle Size ◽  
Gamma Ray ◽  
Soil Particle ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Soil Particle Size ◽  
Gamma Ray Attenuation ◽  
Soil Particle Size Fractions

The effect of aging time on the distribution of pyrene in soil particle-size fractions

Geoderma ◽  
2014 ◽  
Vol 232-234 ◽  
pp. 19-23 ◽  
Author(s):  
Ran Wei ◽  
Jinzhi Ni ◽  
Lan Guo ◽  
Liuming Yang ◽  
Yusheng Yang
Keyword(s):  
Particle Size ◽  
Aging Time ◽  
Soil Particle ◽  
Size Fractions ◽  
Particle Size Fractions ◽  
Soil Particle Size ◽  
Soil Particle Size Fractions
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