Vis-NIR Spectroscopy for Soil Organic Carbon Assessment: A Meta-Analysis

• Published in: Eurasian soil science Vol. 56; no. 11; pp. 1605 - 1617
• Main Authors: Chinilin, A. V., Vindeker, G. V., Savin, I. Yu
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.11.2023; Springer; Springer Nature B.V
• Subjects: Analysis; Analytical methods; Carbon; Carbon content; Earth and Environmental Science; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Infrared spectroscopy; Meta-analysis; Organic carbon; Root-mean-square errors; Scientific papers; Soil; Soil Chemistry; soil organic carbon; Soils; Spectroscopy; Spectrum analysis; Statistical analysis; variance; Variance analysis; proximal soil sensing; prediction; model calibration; validation; algorithm
• ISSN: 1064-2293; 1556-195X
• DOI: 10.1134/S1064229323601841

The research papers assessing the content of soil organic carbon with the help of Vis-NIR spectroscopy approaches are systematically analyzed and subject to meta-analysis. This meta-analysis included 134 studies published in 1986–2022 with a total sample of 709 values of quantitative metrics. The papers have been searched for in databases of scientific periodicals (RSCI, Science Direct, Scopus, and Google Scholar) by the key word combination “Vis-NIR spectroscopy AND soil organic carbon”. The meta-analysis using the nonparametric one-sided Kruskal–Wallis variance analysis in conjunction with nonparametric pairwise method shows the presence of a statistically significant difference between the median values of the accepted quantitative metrics of the predictive power of the models, namely, coefficient of determination ( R 2 cv/val ), root mean square error (RMSE), and the ratio of performance to deviation (RPD). The best performance of the preprocessing method for spectral curves is demonstrated and the estimates of soil organic carbon content obtained by laboratory and field spectroscopies are compared.