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

Authors
  • Wang, Shifang1, 2
  • Cheng, Xu1, 3
  • Zheng, Decong1
  • Song, Haiyan1
  • Han, Ping2
  • Yuen, Peter4
  • 1 (D.Z.)
  • 2 Beijing Municipal Key Laboratory of Agricultural Environment Monitoring, Beijing 100097, China
  • 3 Agricultural Mechanization Schools in Shanxi Province, Pingyao 031100, China
  • 4 Centre of Electronic Warfare, Cranfield University, Shrivenham, Swindon SN6 2LA, UK
Type
Published Article
Journal
Sensors
Publisher
MDPI AG
Publication Date
Aug 26, 2020
Volume
20
Issue
17
Identifiers
DOI: 10.3390/s20174822
PMID: 32858981
PMCID: PMC7506570
Source
PubMed Central
Keywords
License
Green

Abstract

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 p2) and the ratio of prediction to deviation (RPD) for the prediction of SOM from moist soil have achieved R p2 = 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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