<p>Carbon sequestration in soils under agricultural use can contribute to climate change mitigation. However, the spatial-temporal monitoring of soil organic carbon (SOC) requires more efficient data acquisition. The use of soil Vis-NIR spectroscopy is a promising research field in this context. However, the interpretation of the recorded spectral signal with regards to SOC is not trivial due to the complexity of the soil matrix, and factors affecting the measurements under field conditions. A model-building process is required to relate the spectral signal to the SOC content. For this study, spectral on-the-go proximal measurements and soil sampling were conducted on a long-term field experiment (LTE) located in the state of Saxony-Anhalt, Germany. SOC values ranged between 14–25 g kg<sup>−1</sup> due to different fertilization treatments. Partial least squares regression (PLSR) models were built on behalf of spectral laboratory and field measurements conducted with two spectrometers and preprocessed by various methods. A data correction of the field data was done with three different approaches: linear transformation, piecewise direct standardization (PDS), and external parameter orthogonalization (EPO). The models were then thoroughly interpreted with regards to spectral wavelength importance using regression coefficients (RC) and variable importance in projection scores (VIP). The detailed wavelength importance analysis disclosed the challenge of using soil spectroscopy for SOC monitoring. The use of spectrometers with a differing spectral resolution for soil Vis-NIR measurements under varying soil conditions revealed shifts in wavelength importance. Still, some wavelengths related to SOC were extracted (560 nm, 1330 nm, 1400 nm, 1720 nm, and 1900 nm) by various preprocessing methods and were highly important in models trained on both, laboratory, and field measurements. Furthermore, we showed, that the correction of spectral field data with spectral laboratory measurements improved the predictive performance of the models built on behalf of the proximal on-the-go sensing measurements.</p>