Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands

Самсонова, В. П.; Blagoveshchenskii, Yu. N.; Meshalkina, Joulia

doi:10.1134/s1064229317030103

Cited by 63 publications

(33 citation statements)

References 3 publications

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“…Moreover, in order to check the obtained results from the multivariate statistical analyses with the spatial distribution of the soil properties, several researchers also highlighted the use of geostatistical analysis as the most useful tool (Gribov and Krivoruchko, 2012;Martínez-Murillo et al, 2017). Using remote sensing data and Geographic Information Systems (GISs), techniques such as inverse distance weighting (IDW), ordinary kriging, empirical Bayesian kriging and radial basis functions are widely used in soil sciences and other disciplines such as hydrology, geology and environmental sciences (Sağir and Kurtuluş, 2017;Samsonova et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

et al. 2019

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Natural soil-forming factors such as landforms, parent materials or biota lead to high variability in soil properties. However, there is not enough research quantifying which environmental factor(s) can be the most relevant to predicting soil properties at the catchment scale in semi-arid areas. Thus, this research aims to investigate the ability of multivariate statistical analyses to distinguish which soil properties follow a clear spatial pattern conditioned by specific environmental characteristics in a semi-arid region of Iran. To achieve this goal, we digitized parent materials and landforms by recent orthophotography. Also, we extracted ten topographical attributes and five remote sensing variables from a digital elevation model (DEM) and the Landsat Enhanced Thematic Mapper (ETM), respectively. These factors were contrasted for 334 soil samples (depth of 0-30 cm). Cluster analysis and soil maps reveal that Cluster 1 comprises of limestones, massive limestones and mixed deposits of conglomerates with low soil organic carbon (SOC) and clay contents, and Cluster 2 is composed of soils that originated from quaternary and early quaternary parent materials such as terraces, alluvial fans, lake deposits, and marls or conglomerates that register the highest SOC content and the lowest sand and silt contents. Further, it is confirmed that soils with the highest SOC and clay contents are located in wetlands, lagoons, alluvial fans and piedmonts, while soils with the lowest SOC and clay contents are located in dissected alluvial fans, eroded hills, rock outcrops and steep hills. The results of principal component analysis using the remote sensing data and topographical attributes identify five main components, which explain 73.3% of the total variability of soil properties. Environmental factors such as hillslope morphology and all of the remote sensing variables can largely explain SOC variability, but no significant correlation is found for soil texture and calcium carbonate equivalent contents. Therefore, we conclude that SOC can be considered as the best-predicted soil property in semi-arid regions.

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Section: Introductionmentioning

confidence: 99%

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

et al. 2019

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“…Meanwhile, empirical bayesian kriging (EBK) as a method of geostatistical interpolation automates the most difficult aspects of composing an adequate kriging model. Due to the interpolation of available observations, this makes the method independent of trends and offers hope for a significant expansion of the application areas [26]. Although EBK was successfully used for analysis of humus distribution [26] and in benthos mapping [27], few scholars have used this method to study the spatial variability of soil.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the interpolation of available observations, this makes the method independent of trends and offers hope for a significant expansion of the application areas [26]. Although EBK was successfully used for analysis of humus distribution [26] and in benthos mapping [27], few scholars have used this method to study the spatial variability of soil. Thus, this study attempts to apply the OK and EBK methods in a comparative study of soil physical properties in order to explore the best research methods to study the spatial variation of soil properties at a fine scale.…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Yan¹,

Hua²,

Yan³

et al. 2019

Pol. J. Environ. Stud.

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Spatial variability of soil has an important influence on the structure and function of soil. The spatial distribution of soil physical properties provides basic and useful information relevant to soil management and ecological protection. A typical red beds basin was selected for this study, soil samples at 0-20 cm were taken from 150 locations in the northeast part of Nanxiong Basin, in which GIS and geostatistics were used to analyze the spatial variability of the soil physical properties. The results show that the coefficients of variation of soil bulk density, total porosity and capillary porosity are 9.82%, 4.47%, and 3.72%, respectively, which indicate weak variation. Pearson correlation indicated that soil bulk density was significantly positively correlated with soil moisture and capillary water capacity (p<0.01), with correlation coefficients of 0.85 and 0.91, respectively, but was significantly negatively correlated with total porosity, capillary porosity and non-capillary porosity, with correlation coefficients of 0.82, 0.71 and 0.94, respectively (p<0.01). The spatial distributions of soil physical properties using ordinary kriging (OK) and empirical bayesian kriging (EBK) methods were subjected to comparative analysis. In addition, different cross-validation indicators were applied to assess the performance of different interpolation methods. Cross-validation demonstrated that EBK performed better than OK. And EBK produced smaller regions of predicted soil physical properties than OK, highlighting the necessity of choosing the appropriate methods in studying the spatial distribution of soil properties.

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“…Scientists from Lithuania tested several geo-statistical and interpolation methods for monitoring the spatio-temporal evolution of ashes in peat soil after a fire (Martin and Jord, 2013). The problems of assessing the suitability of various interpolation models for studying the spatial distribution of soil properties are devoted to studies of Russian (Simbatova et al, 2016;Samsonova et al, 2017) and Ukrainian (Moskalenko, 2012;Kohan et al, 2013) scientists. However, in the scientific literature there is not enough information about the development of this area of research in the territory of Belarus.…”

Section: Introductionmentioning

confidence: 99%

Efficiency of Interpolation Methods Based on GIS for Estimating of Spatial Distribution of PH in Soil

2019

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The main objective of this study is to review and evaluate three common interpolation methods namely: Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Ordinary Kriging (OK), and generate maps of soil pH using these methods. The accuracy and efficiency of the generated maps have been examined as well as the most fitting technique for estimating spatial distribution of soil pH in the study area is identified. Studies were conducted within the limits of land use of RUP “Uchkhoz BGSHA” (Republic of Belarus, Mogilev region, Goretsky district). The total area of the surveyed territory is 3197.89 hectares. For the analysis data is used about pHKCl of soil solution obtained from materials of an agrochemical survey executed in 2014. Forecasting and visualization of the spatial distribution of pHKCl was carried out using the Geostatistical Analyst module of the ArcGIS software. The experimental anisotropic variograms were calculated to determine the possible spatial structure of soil pH. Based on cross-validation results, a polynomial function was identified as the best variogram model. The model created by the method of radial basis functions turned out to be the most suitable for forecasting purposes (the value of the root-mean-square error was 0.763). In terms of interpolation accuracy, the investigated deterministic and geostatistical methods are located in the next descending row

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Use of empirical Bayesian kriging for revealing heterogeneities in the distribution of organic carbon on agricultural lands

Cited by 63 publications

References 3 publications

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Determining the spatial distribution of soil properties using the environmental covariates and multivariate statistical analysis: a case study in semi-arid regions of Iran

Spatial Variability of Soil Physical Properties Based on GIS and Geo-Statistical Methods in the Red Beds of the Nanxiong Basin, China

Efficiency of Interpolation Methods Based on GIS for Estimating of Spatial Distribution of PH in Soil

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