Improving the spatial prediction accuracy of soil alkaline hydrolyzable nitrogen using GWPCA‐GWRK

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.