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Prediction of the Soil Organic Matter (SOM) Content from Moist Soil Using Synchronous Two-Dimensional Correlation Spectroscopy (2D-COS) Analysis

文献类型： 外文期刊

作者： Wang, Shifang 1 ; Cheng, Xu 1 ; Zheng, Decong 1 ; Song, Haiyan 1 ; Han, Ping 2 ; Yuen, Peter 5 ;

作者机构： 1.Shanxi Agr Univ, Coll Agr Engn, Taigu 030801, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Agr Stand & Testing, Beijing 100097, Peoples R China

3.Beijing Municipal Key Lab Agr Environm Monitoring, Beijing 100097, Peoples R China

4.Agr Mechanizat Sch Shanxi Prov, Pingyao 031100, Peoples R China

5.Shanxi Agr Univ, Coll Software Engn, Taigu 030801, Peoples R China

6.Cranfield Univ, Ctr Elect Warfare, Swindon SN6 2LA, Wilts, England

关键词： two-dimensional correlation spectroscopy; visible-near infrared spectroscopy; partial least square regression; spectral variable selection; moisture effect; soil; soil organic matter

期刊名称：SENSORS （ 影响因子：3.576； 五年影响因子：3.735 ）

ISSN：

年卷期： 2020 年 20 卷 17 期

页码：

收录情况： SCI

摘要： This paper illustrates a simple yet effective spectroscopic technique for the prediction of soil organic matter (SOM) from moist soil through the synchronous 2D correlation spectroscopy (2D-COS) analysis. In the moist soil system, the strong overlap between the water absorption peaks and the SOM characteristic features in the visible-near infrared (Vis-NIR) spectral region have long been recognised as one of the main factors that causes significant errors in the prediction of the SOM content. The aim of the paper is to illustrate how the tangling effects due to the moisture and the SOM can be unveiled under 2D-COS through a sequential correlogram analysis of the two perturbation variables (i.e., the moisture and the SOM) independently. The main outcome from the 2D-COS analysis is the discovery of SOM-related bands at the 597 nm, 1646 nm and 2138 nm, together with the predominant water absorbance feature at the 1934 nm and the relatively less important ones at 1447 nm and 2210 nm. This information is then utilised to build partial least square regression (PLSR) models for the prediction of the SOM content. The experiment has shown that by discarding noisy bands adjacent to the SOM features, and the removal of the water absorption bands, the determination coefficient of prediction (R-p(2)) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achievedR(p)(2)= 0.92 and the RPD = 3.19, both of which are about 5% better than that of using all bands for building the PLSR model. The very high RPD (=3.19) obtained in this study may suggest that the 2D-COS technique is effective for the analysis of complex system like the prediction of SOM from moist soil.

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