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https://doi.org/10.5194/hess-2020-384
https://doi.org/10.5194/hess-2020-384
31 Aug 2020
 | 31 Aug 2020
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

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

Mo Zhang and Wenjiao Shi

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.

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Mo Zhang and Wenjiao Shi
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
 
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Mo Zhang and Wenjiao Shi
Mo Zhang and Wenjiao Shi

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Latest update: 06 Dec 2024
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Short summary
We used generalized linear model (GLM), random forest (RF), and their hybrid patterns (regression kriging, RK) to map soil particle-size fractions (PSF) using isometric log-ratio (ILR) transformed data. RF and GLM did well in accuracy and bias, respectively. RK can modify bias and accuracy for most models. Different ILR transformed data based on sequential binary partitions was also discussed further. This study can reference soil PSF spatial simulation and how to choose the ILR balances.