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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hess-2020-384
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-384
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Aug 2020

31 Aug 2020

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This preprint is currently under review for the journal HESS.

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

Mo Zhang1,2 and Wenjiao Shi1,3 Mo Zhang and Wenjiao Shi
  • 1Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China
  • 3College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

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.

Mo Zhang and Wenjiao Shi

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Latest update: 28 Sep 2020
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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.
We used generalized linear model (GLM), random forest (RF), and their hybrid patterns...
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