An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content

Liu, Wei; Du, Peijun; Zhao, Zhuowen; Zhang, Lianpeng

doi:10.1038/srep23889

Cited by 13 publications

(15 citation statements)

References 42 publications

(58 reference statements)

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“…When the correlation is greater than 0.75, the simulation accuracy of spatial interpolation methods that combine secondary variables is higher than OK. Nevertheless, as shown by our previous research, the integration of secondary variables does not always effectively increase the spatial interpolation accuracy, though there is a relatively strong spatial correlation between secondary variables and the sample data of soil properties [10]. However, in general, an appropriate integration of geo-environmental factors as secondary variables is able to effectively depict soil property boundaries that abruptly change as the geo-environmental factors change.…”

Section: Effectiveness Of Secondary Variables For Spatial Interpolationmentioning

confidence: 99%

“…Its interpolation accuracy is usually higher than that of a single interpolation model [10]. Thus, it has great advantages for conducting interpolation with multiple models.…”

Section: Performance Of Multi-model Integration For Reducing Predictimentioning

confidence: 99%

“…Previous work by Shi et al [1] demonstrated the effectiveness of incorporating land use type and soil type to improve interpolation simulation of soil properties. In addition, many studies have identified topography as an important auxiliary element [3,19], but previous research results suggest it is not a key factor in the study area [10]. Therefore, integration of secondary variables in this study should have an important influence on interpolation accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…However, all of these represent global interpolation models, which are difficult to adapt to landform complexity areas. Utilizing the advantages of ensemble learning for regression, we combined a series of interpolation models to carry out interpolation simulation of the spatial variation in soil properties and verified the reliability of multi-model integration [10]. Despite this, issues still remain; for example, in previous work, we only conducted a global regression integration of various interpolation models, with limited consideration of discontinuous space and spatial variation problems.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Surface Modeling of Soil Properties in Complex Landforms

Liu

Zhang

Yan

et al. 2017

IJGI

Self Cite

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Spatial discontinuity often causes poor accuracy when a single model is used for the surface modeling of soil properties in complex geomorphic areas. Here we present a method for adaptive surface modeling of combined secondary variables to improve prediction accuracy during the interpolation of soil properties (ASM-SP). Using various secondary variables and multiple base interpolation models, ASM-SP was used to interpolate soil K + in a typical complex geomorphic area (Qinghai Lake Basin, China). Five methods, including inverse distance weighting (IDW), ordinary kriging (OK), and OK combined with different secondary variables (e.g., OK-Landuse, OK-Geology, and OK-Soil), were used to validate the proposed method. The mean error (ME), mean absolute error (MAE), root mean square error (RMSE), mean relative error (MRE), and accuracy (AC) were used as evaluation indicators. Results showed that: (1) The OK interpolation result is spatially smooth and has a weak bull's-eye effect, and the IDW has a stronger 'bull's-eye' effect, relatively. They both have obvious deficiencies in depicting spatial variability of soil K + . (2) The methods incorporating combinations of different secondary variables (e.g., ASM-SP, OK-Landuse, OK-Geology, and OK-Soil) were associated with lower estimation bias. Compared with IDW, OK, OK-Landuse, OK-Geology, and OK-Soil, the accuracy of ASM-SP increased by 13.63%, 10.85%, 9.98%, 8.32%, and 7.66%, respectively. Furthermore, ASM-SP was more stable, with lower MEs, MAEs, RMSEs, and MREs. (3) ASM-SP presents more details than others in the abrupt boundary, which can render the result consistent with the true secondary variables. In conclusion, ASM-SP can not only consider the nonlinear relationship between secondary variables and soil properties, but can also adaptively combine the advantages of multiple models, which contributes to making the spatial interpolation of soil K + more reasonable.

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Section: Effectiveness Of Secondary Variables For Spatial Interpolationmentioning

confidence: 99%

“…Its interpolation accuracy is usually higher than that of a single interpolation model [10]. Thus, it has great advantages for conducting interpolation with multiple models.…”

Section: Performance Of Multi-model Integration For Reducing Predictimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Surface Modeling of Soil Properties in Complex Landforms

Liu

Zhang

Yan

et al. 2017

IJGI

Self Cite

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“…For the preparation of soil maps in the present study, IDW was used to estimate the variables pH, EC and alkalinity over space. The IDW method is a non-geostatistical interpolation method based on the fact that the local impact of a variable gradually disappears with the increase in distance (Liu, 2016). The methodology incorporates soil scientific knowledge and provides a reliable logical framework to the mapping of continuous surfaces in a quantitative approach (Mora-Vallejo, 2008).…”

Section: Digital Soil Mapping Using the Idw Interpolation Methodsmentioning

confidence: 99%

Assessment and monitoring of land degradation using geospatial technology in Bathinda district, Punjab, India

Ahmad

Pandey

2018

Solid Earth

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Abstract. Land degradation leads to alteration of ecological and economic functions due to a decrease in productivity and quality of the land. The aim of the present study was to assess land degradation with the help of geospatial technology -remote sensing (RS) and geographical information system (GIS) -in Bathinda district, Punjab. The severity of land degradation was estimated quantitatively by analyzing the physico-chemical parameters in the laboratory to determine saline or salt-free soils and calcareous or sodic soils and further correlating them with satellite-based studies. The pH varied between 7.37 and 8.59, electrical conductivity (EC) between 1.97 and 8.78 dS m −1 and the methyl orange or total alkalinity between 0.070 and 0.223 (HCO

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Spatially distributed modeling of soil organic carbon across China with improved accuracy

Zhang

Jiang

et al. 2017

J Adv Model Earth Syst

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There is a need for more detailed spatial information on soil organic carbon (SOC) for the accurate estimation of SOC stock and earth system models. As it is effective to use environmental factors as auxiliary variables to improve the prediction accuracy of spatially distributed modeling, a combined method (HASM_EF) was developed to predict the spatial pattern of SOC across China using high accuracy surface modeling (HASM), artificial neural network (ANN), and principal component analysis (PCA) to introduce land uses, soil types, climatic factors, topographic attributes, and vegetation cover as predictors. The performance of HASM_EF was compared with ordinary kriging (OK), OK, and HASM combined, respectively, with land uses and soil types (OK_LS and HASM_LS), and regression kriging combined with land uses and soil types (RK_LS). Results showed that HASM_EF obtained the lowest prediction errors and the ratio of performance to deviation (RPD) presented the relative improvements of 89.91%, 63.77%, 55.86%, and 42.14%, respectively, compared to the other four methods. Furthermore, HASM_EF generated more details and more realistic spatial information on SOC. The improved performance of HASM_EF can be attributed to the introduction of more environmental factors, to explicit consideration of the multicollinearity of selected factors and the spatial nonstationarity and nonlinearity of relationships between SOC and selected factors, and to the performance of HASM and ANN. This method may play a useful tool in providing more precise spatial information on soil parameters for global modeling across large areas.

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An Adaptive Weighting Algorithm for Interpolating the Soil Potassium Content

Cited by 13 publications

References 42 publications

Adaptive Surface Modeling of Soil Properties in Complex Landforms

Adaptive Surface Modeling of Soil Properties in Complex Landforms

Assessment and monitoring of land degradation using geospatial technology in Bathinda district, Punjab, India

Spatially distributed modeling of soil organic carbon across China with improved accuracy

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