Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA-GWRK,Soil Science Society of America Journal

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Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA-GWRK
Soil Science Society of America Journal ( IF 2.9 ) Pub Date : 2020-10-27 , DOI: 10.1002/saj2.20189
Jian Chen _{1,

2} , Mingkai Qu _{1,

2} , Jianlin Zhang ₁ , Enze Xie _{1,

2} , Yongcun Zhao _{1,

2} , Biao Huang _{1,

2}

Affiliation

Principal component analysis-multiple linear regression (PCA-MLR) is usually used to weaken the multi-collinearity effects among auxiliary variables in a regression prediction. However, both PCA and MLR in this model are only built on variable space rather than geographical space. When used in the spatial prediction of soil properties, PCA-MLR usually cannot effectively capture the spatially non-stationary structures among auxiliary variables and spatially non-stationary relationships between the target variable and principal component scores. Moreover, PCA-MLR may ignore the potentially valuable regression residual. To address these limitations, this study first proposed geographically weighted principal component analysis-geographically weighted regression kriging (GWPCA-GWRK) for the spatial prediction of soil alkaline hydrolyzable nitrogen (AN) in Shayang County, China. Then, the spatial prediction accuracy of GWPCA-GWRK was compared with those of the following five models: ordinary kriging (OK), co-kriging (CoK), PCA-MLR, PCA-graphically weighted regression (PCA-GWR), and GWPCA-GWR. Results showed that (i) eight variables were determined as auxiliary data by a geodetector; (ii) the spatially non-stationary relationships among the eight auxiliary variables were revealed by the results of the local correlation analysis, Monte Carlo test, and GWPCA; (iii) GWPCA-GWRK provided the lowest prediction error (RMSE = 18.80 mg kg⁻¹, MAE = 12.79 mg kg⁻¹) and highest Lin's concordance correlation coefficient (LCCC; 0.75); (iv) relative improvement accuracies over the traditionally-used OK were 19.74% for GWPCA-GWRK, 16.42% for GWPCA-GWR, 8.09% for PCA-GWR, −3.67% for PCA-MLR, and 4.70% for CoK. It is concluded that the proposed GWPCA-GWRK model is an effective spatial predictor, which can adequately extract the main information of the multiple auxiliary variables in a large-scale area.

更新日期：2020-10-27

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