Prediction of total nitrogen in cropland soil at different levels of soil moisture with Vis/NIR spectroscopy

Visible/near-infrared (Vis/NIR) spectroscopy has been proven to be an effective technique for soil total nitrogen (TN) content estimation in the laboratory conditions. However, the transferability of this technique from laboratory study to field application is complicated by soil moisture effects. This study aims to compare the performance of four spectral transformation strategies, namely, Savitzky–Golay (SG) smoothing, SG smoothing followed by first derivative (FD), orthogonal signal correction (OSC), and generalized least squares weighting (GLSW), in the removal of soil moisture effects on TN estimation. The spectral transformations were applied on 8 sets of spectral reflectance measured from 62 soil samples at 8 moisture levels. The air-dried set was used for partial least squares regression (PLSR) calibration, whereas the other seven sets with moisture gradients were used for external validations. Results show that the SG-PLSR model cannot be transferred from the air-dried samples to the samples with moisture gradients. The FD-PLSR model showed fair TN prediction performance, with five out of seven residual prediction deviations (RPD) that are greater than 1.4. Both OSC-PLSR and GLSW-PLSR had good transferability to the moist samples. More specifically, the GLSW-PLSR model (mean of , root mean square error for prediction [RMSEP] = 0.262, and RPD = 1.885) outperformed the OSC-PLSR model (mean of , RMSEP = 0.277, and RPD = 1.780). The results demonstrate the value of OSC and GLSW in eliminating the effects of moisture on TN estimation, and the GLSW-PLSR is recommended for a better Vis/NIR estimation of TN content under different soil moisture conditions.

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