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Modeling to Correct the Effect of Soil Moisture for Predicting Soil Total Nitrogen by Near-Infrared Spectroscopy by Rongnian Tang; Kaixuan Jiang; Chuang Li; Xiaowei Li; Jingjin Wu is a Engineering article available to read on EtoBox.

What is Modeling to Correct the Effect of Soil Moisture for Predicting Soil Total Nitrogen by Near-Infrared Spectroscopy about?

Near-infrared (NIR) spectroscopy can improve the efficiency of soil property prediction, such as that of soil total nitrogen (TN) content. However, soil spectra are very sensitive to soil moisture content, which is a crucial factor affecting the accuracy of soil nutrient composition prediction. In response to this issue, the goal of this study is to identify the best model to correct the effect of soil moisture for predicting soil total nitrogen by near-infrared spectroscopy. The 107 collected soil samples were divided into six different water content (0%, 5%, 10%, 15%, 20%, and 25%) sample groups. Then, five correction methods, including direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), spectral space transformation (SST), and slope/bias (S/B), were executed. Finally, partial least squares regression (PLSR) models were established to forecast TN content. The results showed that SST could minimize the influence of moisture. Furthermore, SST–PLSR had the best TN content prediction accuracy: Rp2 (the coefficient of determination of the prediction set) in the range of 0.81–0.82, RMSEP (the root mean square error of the pre

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Author

Rongnian Tang; Kaixuan Jiang; Chuang Li; Xiaowei Li; Jingjin Wu

Publisher

MDPI AG

Published

2023

Language

EN

Field

Engineering (Physical Sciences)