Login | Request Account (DAF staff only)

Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions

View Altmetrics

Tahmasbian, I., Xu, Z., Boyd, S., Zhou, J., Esmaeilani, R., Che, R. and Hosseini Bai, S. (2018) Laboratory-based hyperspectral image analysis for predicting soil carbon, nitrogen and their isotopic compositions. Geoderma, 330 . pp. 254-263. ISSN 0016-7061

Full text not currently attached. Access may be available via the Publisher's website or OpenAccess link.

Article Link: https://doi.org/10.1016/j.geoderma.2018.06.008

Publisher URL: https://www.sciencedirect.com/science/article/pii/S0016706117315732

Abstract

The common methods of determining soil carbon (C), nitrogen (N) and their isotopic compositions (δ13C and δ15N) are expensive and time-consuming. Therefore, alternative low-cost and rapid methods are sought to address this issue. This study aimed to investigate the potential of hyperspectral image analysis to predict soil total carbon (TC), total nitrogen (TN), δ13C and δ15N. Hyperspectral images were captured from 96 ground soil samples using a laboratory-based visible to near-infrared (VNIR) hyperspectral camera in the spectral range of 400–1000 nm. Partial least squares regression (PLSR) models were developed to correlate the values of TC, TN, δ13C and δ15N, obtained from isotope ratio mass spectrometry method, with their spectral reflectance. The developed models provided acceptable predictions with high coefficient of determination (R2c) and low root mean square error (RMSEc) of calibration set for TC (R2c = 0.82; RMSEc = 1.08%), TN (R2c = 0.87; RMSEc = 0.02%), δ13C (R2c = 0.82; RMSEc = 0.27‰) and δ15N (R2c = 0.90; RMSEc = 0.29‰). The prediction abilities of the models were then evaluated using the spectra of an external test set (24 samples). The models provided excellent predictions with high R2t and ratio of performance to deviation (RPD) of test set for TC (R2t = 0.76; RPD = 2.02), TN (R2t = 0.86; RPD = 2.08), δ13C (R2t = 0.80; RPD = 2.00) and δ15N (R2t = 0.81; RPD = 1.94). The results indicated that the laboratory-based hyperspectral image analysis has the potential to predict soil TC, TN, δ13C and δ15N.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Animal Science
Keywords:Native forest Chemometrics Multivariate analysis PLSR, VNIR, δC, δN
Subjects:Agriculture > Agriculture (General) > Special aspects of agriculture as a whole > Inventions
Agriculture > Agriculture (General) > Soils. Soil science
Agriculture > Agriculture (General) > Soils. Soil science > Soil and crops. Soil-plant relationships. Soil productivity
Agriculture > Agriculture (General) > Fertilisers
Live Archive:27 May 2024 05:28
Last Modified:27 May 2024 05:28

Repository Staff Only: item control page