scholarly journals Spatial Prediction of Soil Organic Matter Using a Hybrid Geostatistical Model of an Extreme Learning Machine and Ordinary Kriging

2017 ◽  
Vol 9 (5) ◽  
pp. 754 ◽  
Author(s):  
Ying-Qiang Song ◽  
Lian-An Yang ◽  
Bo Li ◽  
Yue-Ming Hu ◽  
An-Le Wang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Extreme Learning Machine ◽  
Ordinary Kriging ◽  
Spatial Prediction ◽  
Geostatistical Model ◽  
Learning Machine

Spatial prediction of soil organic matter in northern Kazakhstan based on topographic and vegetation information

2007 ◽  
Vol 53 (3) ◽  
pp. 289-299 ◽  
Author(s):  
Yusuke Takata ◽  
Shinya Funakawa ◽  
Kanat Akshalov ◽  
Norio Ishida ◽  
Takashi Kosaki
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Spatial Prediction ◽  
Northern Kazakhstan

Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information

Geoderma ◽  
2012 ◽  
Vol 171-172 ◽  
pp. 35-43 ◽  
Author(s):  
Shiwen Zhang ◽  
Yuanfang Huang ◽  
Chongyang Shen ◽  
Huichun Ye ◽  
Yichun Du
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Auxiliary Information ◽  
Spatial Prediction ◽  
Categorical Variables ◽  
Terrain Indices

scholarly journals Comparison of Different Spatial Regression Methods for Predicting Organic Matter Content in Soil

2022 ◽  
Author(s):  
Xumeng Zhang ◽  
Wuping Zhang ◽  
Mingjing Huang ◽  
Li Gao ◽  
Lei Qiao ◽  
...  
Keyword(s):  
Organic Matter ◽  
Spatial Distribution ◽  
Soil Organic Matter ◽  
Land Use Planning ◽  
Prediction Accuracy ◽  
Meteorological Data ◽  
Organic Matter Content ◽  
Spatial Prediction ◽  
Matter Content ◽  
Soil Organic Matter Content

Abstract Dynamic changes in soil organic matter content affects the sustainable supply of soil water and fertilizer and impacts the stability of soil ecological function. Understanding the spatial distribution characteristics of soil organic matter will help deepen our understanding of the differences in soil organic matter content, soil formation law; such understanding would be useful for rational land use planning. Taking terrain data, meteorological data, and remote sensing data as auxiliary variables and the ordinary Kriging (OK) method as a control, this study compares the spatial prediction accuracies and mapping effects of various models (MLR, RK, GWR, GWRK, MGWR, and MGWRK) on soil organic matter. Our results show that the spatial distribution trend of soil organic matter predicted by each model is similar, but the prediction of composite models can reflect more mapping details than that of unitary models. The OK method can provide better support for spatial prediction when the sampling points are dense; however, the local models are superior in dealing with spatial non-stationarity. Notably, the MGWR model is superior to the GWR model, but the MGWRK model is inferior to the GWRK model. As a new method, the prediction accuracy of MGWRK reached 47.72% for the OK and RK methods and 40.08% for the GWRK method. The GWRK method achieved a better prediction accuracy. The influence mechanism of soil organic matter is complex, but the MGWR model more clearly reveals the complex nonlinear relationship between soil organic matter content and factors influencing it. This research can provide reference methods and mapping technical support to improve the spatial prediction accuracy of soil organic matter.

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