Soil carbon sequestration is one possible way of reducing greenhouse gas emissions in the atmosphere. However, to evaluate the real benefits offered by these methods (new agricultural practices, reforestation, etc.), there is a need in rapid, precise, and low-cost analytical tools. Near-infrared spectroscopy (NIRS) is now commonly used to measure different physical and chemical parameters of soils, including carbon content. However, prediction model accuracy is insufficient for NIRS to replace routine laboratory analysis and/or to make in situ measurements, whatever the type of soil. One of the biggest issues that need to be addressed concerns the calibration process: how does the mathematical method or the sample selection influence the model quality? In most cases, there are not a lot of thoughts put into the choice of the mathematical method, which is often made empirically (test and try). It is therefore essential to return to fundamental laws governing spectrum formation in order to optimize calibration. Indeed, the light/matter interactions are at the basis of the resulting linear modeling. This chapter reviews and discusses the basic theoretical concepts underpinning NIRS and linear chemometric modeling in the specific context of soil: (i) light scattering due to soil particles causes departure in the assumed linear relationship between the spectrum and the carbon content, and (ii) the other classical linear regression assumptions (constant residual variance, normal error distribution, etc.) are also put into question. Regarding these specific issues, the different chemometric methods presented as possible solutions to perform better calibration model are discussed, from linear methods associated with various preprocessing, local methods, or nonlinear methods.