Prediction of tropical volcanic soil organic carbon stocks by visible-near- and mid-infrared spectroscopy

•SOC stocks were predicted in situ (visNIRS) and in the laboratory (visNIRS and MIRS).•SRO minerals, especially allophanes, were predicted by NIRS and MIRS.•SRO minerals, especially allophanes, are proxy of bulk density and SOC stock. Assessing soil organic carbon (SOC) stocks is a methodological issue for SOC monitoring at regional scale but crucial for global agendas of SOC sequestration to mitigate climate change and reduce food insecurity. The ‘4 per mille Initiative: Soils for Food Security and Climate’, highlighted agricultural soil as a major lever for climate action and the need to assess SOC stock at different spatial and temporal scale. Infrared spectroscopy appeared as a promising tool to address this methodological issue. This work aimed to evaluate the potential of visible-near-infrared (VNIR) and mid-infrared (MIR) spectroscopic measurement methods to predict SOC stock and its variables (SOC content and bulk density) in tropical volcanic soils of ‘La Réunion’ island. The diversity of agricultural soils of ‘La Réunion’ was captured in the sample set (n = 95) with soil orders such as Andosols, Cambisols and Ferralsols. Partial least squares regressions (PLSR) with leave-one-out cross validation were used to build prediction models. With RPD higher than 2, the present study showed good prediction accuracy of models by MIR and VNIR spectroscopy of SOC content, bulk density and SOC stock for measurements in the laboratory or in the field. Accurate and direct SOC stock predictions were achieved on dried and sieved soil samples with MIR spectroscopy (RPD = 2.25; R2cv = 0.80; RMSEcv = 0.69 KgC m−2) and VNIR spectroscopy (RPD = 2.74; R2cv = 0.87; RMSEcv = 0.61 KgC m−2) but also directly on cores in the field with VNIR spectroscopy (RPD = 3.29; R2cv = 0.91; RMSEcv = 0.51 KgC m−2). This unexpected ability to predict directly SOC stocks by infrared spectroscopy can be partly explained by the high SOC content coupled with the large variation of SOC content and bulk density, providing a large range for those variables, and then a higher predictability. Yet these results questioned the underlying drivers of the bulk density and SOC stock, both being largely physical parameters supposed to be hardly predictable by infrared spectroscopy. Analyses of mean spectra and regression coefficients, combined with amorphous product (Alo, Feo, Sio, Alp, Fep) prediction models, demonstrated that these were detected in the spectra and were the drivers of SOC stock accurat