Comparison of Geographically Weighted Regression and Regression Kriging for Estimating the Spatial Distribution of Soil Organic Matter

Ku, Wang; Zhang, Chuanrong; Li, Weidong

doi:10.2747/1548-1603.49.6.915

Cited by 52 publications

(21 citation statements)

References 34 publications

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“…developed an improved approach by integrating geostatistical techniques with the Intergovernmental Panel on Climate Change (IPCC) carbon inventory approach to estimate the reference carbon stocks for a seven state area of the Midwestern United States. Wang et al (2012) compared GWR with RK for estimating the spatial distribution of SOM in Longyan, China. Mishra and Riley (2014) used spatially referenced soil profile description data and environmental variables in a GWR approach to predict the spatial variability of active-layer thickness across Alaska.…”

Section: Comparisons Of Model Performance In Socd Mappingmentioning

confidence: 99%

“…Similar conclusions were reported by , who compared the prediction accuracy between a simple averaging (SA) and RK to estimate the SOC stocks in the Midwestern U.S., and the results showed that RK method was more accurate in representing the heterogeneity of SOC stocks, because RK incorporates environmental variables that influence SOC stocks in the statistical analysis. Wang et al (2012) compared GWR with RK for estimating the spatial distribution of SOM using field-sample data in SOM and auxiliary data in correlated environmental variables. And the results showed that GWR was a relatively better method and could provide promising results for SOM prediction in comparison with RK.…”

Section: Comparisons Of Model Performance In Socd Mappingmentioning

confidence: 99%

“…For examples, Wang et al (2012) chose elevation, ferrous mineral index, the distance from sample point to river, slope, and NDVI as the auxiliary variables to estimate the spatial distribution of SOM. Kumar et al (2012) demonstrated in their study that slope, NDVI, and temperature were the main factors influencing the spatial distributions of SOCD.…”

Section: The Influence Of Different Explanatory Variables On the Socdmentioning

confidence: 99%

See 2 more Smart Citations

Comparing geospatial techniques to predict SOC stocks

Liu

Guo

Jiang

et al. 2015

Soil and Tillage Research

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Section: Comparisons Of Model Performance In Socd Mappingmentioning

confidence: 99%

Section: Comparisons Of Model Performance In Socd Mappingmentioning

confidence: 99%

Section: The Influence Of Different Explanatory Variables On the Socdmentioning

confidence: 99%

See 1 more Smart Citation

Comparing geospatial techniques to predict SOC stocks

Liu

Guo

Jiang

et al. 2015

Soil and Tillage Research

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“…One of advantages of using the logit transformation is that the predicted value can be fixed by specifying physical limits (z min ,z max ) based on prior knowledge [22]. According to previous studies, average annual rainfall in the study area is generally within 100-1000 mm [29,30]. Therefore, z min was set to 100 mm and z max was set to 1000 mm.…”

Section: Logit Transformation and Exploratory Data Analysis Methodsmentioning

confidence: 99%

“…Under its influence, dry and cold winds in winter are followed by frequent and intense rainfall in summer [27]. Annual precipitation recorded by meteorological stations ranges from 200 mm to 800 mm and decreases from the southeast to the northwest of the Loess Plateau [28,29]. In order to mitigate the prediction error of "edge effects" in interpolation [15], a buffer area with a 100 km bandwidth was considered around the Loess Plateau.…”

Section: Introductionmentioning

confidence: 99%

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Jin

Zhang

Shi

et al. 2016

Water

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Abstract:Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK) and geographically weighted regression Kriging (GWRK) methods were employed using precipitation data from the period 1980-2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM), normalized difference vegetation index (NDVI), solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

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Implications of choosing different interpolation methods: A case study for soil test phosphorus

Matcham

Subburayalu

Culman

et al. 2021

Crop Forage & Turfgrass Mgmt

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Many farmers have their fields grid soil sampled to plan for variable rate P fertilizer application. Grid soil samples are often interpolated to create fertilizer application maps. However, most farmers and other practitioners do not compare interpolation methods. The objective of this study was to evaluate the performance of different grid soil sampling interpolation methods on P fertilizer prescription maps. Grid soil samples were collected from six fields and interpolated via geographically weighted regression (GWR), random forest (RF), and inverse distance weighting (IDW). At four out of six site-years, the root mean square error of soil test P (STP) was 7 to 16% lower for the GWR method compared with the RF method, and GWR identified the low-STP areas better than RF. Geographically weighted regression may outperform RF and IDW because of its lower error and reduced sensitivity to individual high-and low-STP values. Evaluating multiple interpolation techniques and comparing maps both visually and using global error rates can improve the decisions made by farmers and other practitioners.

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Comparison of Geographically Weighted Regression and Regression Kriging for Estimating the Spatial Distribution of Soil Organic Matter

Cited by 52 publications

References 34 publications

Comparing geospatial techniques to predict SOC stocks

Comparing geospatial techniques to predict SOC stocks

Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

Implications of choosing different interpolation methods: A case study for soil test phosphorus

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