Abstract

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 (R²_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.

中文翻译：

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用于土壤有机碳评估的可见近红外光谱：荟萃分析

摘要

对借助可见-近红外光谱方法评估土壤有机碳含量的研究论文进行了系统分析并进行了荟萃分析。这项荟萃分析包括 1986 年至 2022 年发表的 134 项研究，样本总数为 709 个定量指标值。这些论文已在科学期刊数据库（RSCI、Science Direct、Scopus 和 Google Scholar）中通过关键词组合“Vis-NIR Spectroscopy AND土壤有机碳”进行检索。使用非参数单边 Kruskal-Wallis 方差分析与非参数成对方法相结合的荟萃分析表明，模型预测能力的公认定量指标的中值之间存在统计显着差异，即测定 ( R ² _cv/val )、均方根误差 (RMSE) 以及性能与偏差之比 (RPD)。论证了光谱曲线预处理方法的最佳性能，并比较了实验室和现场光谱获得的土壤有机碳含量的估计值。