Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

Chai, Xurong; Shen, Chongyang; Yuan, Xiaotao; Huang, Yuanfang

doi:10.1016/j.geoderma.2008.09.018

Cited by 67 publications

(45 citation statements)

References 28 publications

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“…Which method is the priority selection still needs further discussion in the hilly red soil region. Although some studies have been reported in other regions [15,27], the results may not be suitable for the red soil region due to the complex terrain and fast changing soil types and land-use patterns. A comprehensive comparative study for different interpolation methods in this area is still necessary and has important practical significance.…”

Section: Introductionmentioning

confidence: 89%

“…For the polygon-based method, soil property data can be linked to soil type, land-use pattern, climate zone, agricultural region, and even administrative region unit and regular grid [19,[23][24][25]. Meanwhile, kriging has also evolved into the methods such as universal kriging (UK), co-kriging (CK), regression kriging (RK), and kriging combined with other auxiliary information (soil, land-use, vegetation type, and other related information) [26][27][28][29]. After being modified, the polygon-based and kriging methods are usually much more adaptive in the spatial prediction of SOC in some soil regions.…”

Section: Introductionmentioning

confidence: 99%

“…After being modified, the polygon-based and kriging methods are usually much more adaptive in the spatial prediction of SOC in some soil regions. Some scholars also have compared the performance of the different derivative methods in corresponding soil regions [15,27,30,31]. Zhao et al evaluated the SOC prediction uncertainty of different prediction methods including linking soil profiles to polygons of a digital soil map, multiple linear regression (MLR), universal kriging (UK), and regression-kriging (RK) in Hebei Province, China [15].…”

Section: Introductionmentioning

confidence: 99%

“…He found that the prediction uncertainty of RK was less than the other methods. Chai et al predicted the spatial distribution of soil organic matter in the Pinggu district of Beijing with several methods including regression-kriging and empirical best linear unbiased predictor, and evaluated their performance [27]. Kumar et al compared ordinary kriging and co-kriging methods using different terrain attributes as auxiliary factors in a watershed of the Himalayan landscape in the western part of India, and pointed out that co-kriging with terrain attributes could greatly improve the prediction accuracy of the soil total carbon [31].…”

Section: Introductionmentioning

confidence: 99%

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Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

Zhang

Wang

et al. 2018

Sustainability

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At present, various interpolation methods have been used for obtaining the spatial variability of soil organic carbon (SOC) in some regions. However, systematic research on accuracy comparison and the influence of method selection on revealing SOC variability is still lacking for the hilly red soil region of China. In this study, the spatial distributions of SOC were interpolated via three kriging methods (ordinary kriging (OK), kriging combined with soil type information (KSt), and with land-use information (KLu)) and three polygon-based methods (linking sample points data to the sampling grid (PGr), to soil type polygon (PSt), and to land-use polygon (PLu)) in Yujiang County, Jiangxi Province, China. To illustrate the influence of method selection on revealing SOC variability, the prediction efficiencies of these methods were compared with soil validation samples. The results showed that the KLu and PLu had high prediction accuracy, followed immediately by the KSt and PSt, while the OK and PGr had very low accuracies. Meanwhile, the SOC distribution contours obtained by KLu, PLu, KSt, and PSt could better reflect the real conditions of the study area, as they were consistent with the variation of land-use or soil types. However, the contours from the OK and PGr had poor performance with sketchy borderlines and regular homogeneous grid cells, respectively. Therefore, great differences exist in the prediction efficiency of different methods, and the interpolation method selection has an important impact on revealing SOC variability in the hilly red soil region of China. The results can provide a useful reference for the efficient SOC prediction and simulation in similar soil regions.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

Zhang

Wang

et al. 2018

Sustainability

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“…The E-BLUP is, in effect, a combination of a regression-type prediction from the covariation and a kriging-type prediction from the random effect. This was discussed in the context of soil information by Lark et al (2006), and the approach has been applied in various studies (e.g., Chai et al, 2008). In addition to the predictor, a prediction error variance is also computed which provides a measure of the uncertainty of the prediction.…”

Section: Introductionmentioning

confidence: 99%

A linear mixed model, with non-stationary mean and covariance, for soil potassium based on gamma radiometry

2010

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Abstract. In this paper we present a linear mixed model for the potassium content of soil across a large region of eastern England in which the mean is modelled as a linear function of the passive gamma-ray emissions of the earth surface in the energy interval commonly associated with potassium decay. Non-stationary models are proposed for the random effect, which is the variation not captured by this regression. Specifically, we assume that the local spectrum of the standardized random effect can be obtained by tempering a common (stationary) spectrum, that is to say raising its values to a power, the tempering parameter, which is itself modelled as a linear function of the radiometric data. This allows the "smoothness" of the random effect to vary locally. In addition the local spatially correlated variance and "nugget" variance (apparently uncorrelated given the resolution of the sampling) can also be modelled as a function of the radiometric data. Using the radiometric signal as a covariate gave some improvement in the precision of predictions of soil potassium at validation sites. In addition, there was evidence that nonstationary models for the random effect fitted the data better than stationary models, and this difference was statistically significant. Non-stationary models also appeared to describe the error variance of predictions at the validation sites better. Further work is needed on selection among alternative nonstationary models, since simple procedures used here, based on comparing log-likelihood ratios of nested models and the Akaike information criterion for non-nested models, did not identify the model which gave the best account of the prediction error variances at validation sites.

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Ancillary information improves kriging on soil organic carbon data for a typical karst peak cluster depression landscape

et al. 2012

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Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

Cited by 67 publications

References 28 publications

Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

Influence of the Selection of Interpolation Method on Revealing Soil Organic Carbon Variability in the Red Soil Region, China

A linear mixed model, with non-stationary mean and covariance, for soil potassium based on gamma radiometry

Ancillary information improves kriging on soil organic carbon data for a typical karst peak cluster depression landscape

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