《Table 2 Parameters of multiple linear regression model (MLR) and geographically weighted regression

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《Combining Environmental Factors and Lab VNIR Spectral Data to Predict SOM by Geospatial Techniques》

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Notes:VIF:variance inflation factor；SE:standard error，CV:coefficient of variation；ILUT:the index of land use type，LSWI:land surface water index，EVI:enhanced vegetation index，NDC:the nearest distance to construction land，RDLS:roughness and relief ampli

PLSR and GWR were used to construct the prediction models of SOM by using the spectral reflectance，and the parameters are shown in Table 3.The first four PCs of the spectral reflectance were chosen as the auxiliary variables，since the accumulating contribution rate was80.85%and the eigenvalues were bigger than 1.The coefficients of intercept，PC1，PC2，PC3 and PC4，were30.73，3.49，2.90，2.92，and 2.26，respectively.PC1 had the biggest influence on SOM among all PCs.Because the datasets had been transformed，a p-value and VIF value equal to 0 and a t-value greater than 1 would suggest a statistically significant role of the PCs in the PLSR model.The PCs also had different influences on the SOM in different geographical locations based on the coefficients in GWR.The descending order of the mean values was PC1（3.57），PC3（3.01），PC2（2.92）and PC4（2.33）.PC1 had the strongest influence on SOM，and PC4had the smallest influence.Meanwhile，coefficients of PC3had the greatest degree of variation（CV=10.61%），and coefficients of PC1 had the smallest variation（CV=5.10%）.All of these PCs had a positive relationship with SOM since their coefficients were positive.