Applying Laser-Induced Breakdown Spectroscopy for Rapid Large-Scale Determination of Soil Organic Carbon

Abstract Soil plays a critical role in agricultural productivity and global carbon cycling, acting as a major carbon sink. Accurate soil analysis is essential for sustainable land management, yet conventional laboratory methods for determining soil carbon, such as Walkley-Black and loss-on-ignition (LOI), are time-consuming, chemically intensive, and costly. Laser-Induced Breakdown Spectroscopy (LIBS) has emerged as a promising alternative due to its rapid, non-destructive, and multi-elemental analytical capabilities. This case study evaluates LIBS for soil organic carbon (SOC) determination across 23 European countries, highlighting its potential for large-scale soil monitoring. Key findings indicate that LIBS provides reliable SOC predictions, particularly for low-carbon soils (<5% SOC), although nonlinearity at higher concentrations necessitates correction algorithms. Multivariate data analysis (MVDA) techniques, such as Partial Least Squares Regression (PLSR), can enhance prediction accuracy by accounting for spectral interferences and soil mineralogy. Additionally, previous studies have demonstrated that elemental signatures beyond carbon, such as aluminum and calcium emission lines, can indirectly influence SOC prediction, underscoring the need for comprehensive calibration models. Despite its advantages in speed, scalability, and minimal sample preparation, LIBS accuracy is constrained by reference method uncertainties and matrix effects. Addressing these challenges requires improved calibration strategies, integration with complementary spectroscopic techniques, and advances in machine learning-driven modeling. The development of portable LIBS instruments further extends its application to field-based soil analysis. As the demand for soil analysis increases, LIBS, coupled with robust data analytics, has the potential to revolutionize precision agriculture by enabling real-time, cost-effective soil property assessment for enhanced land management and agronomic applications. Information © The Author 2025