Improving soil organic carbon mapping with a field‐specific calibration approach through diffuse reflectance spectroscopy and machine learning algorithms

Detailed mapping of soil attributes is often not viable due to the high cost of wet‐chemical laboratory analysis, which requires a large number of samples. Thus, we evaluated whether the prediction of SOC contents through field‐specific diffuse reflectance spectroscopy (DRS) can increase the amount of samples available to SOC mapping through data interpolation. For such, we tested the performance of the partial least squares regression (PLSR), random forest (RF) and gradient boosting tree (GBT) algorithms to model and predict SOC. The field‐specific calibration approach proposed here proved to be suitable for predicting SOC content on soil samples, reducing the dependence on wet‐chemical soil laboratory analyses for mapping. With such SOC content prediction, the higher amount of samples to be used for spatial interpolation can be increased, leading to more accurate SOC maps that can be applied for site‐specific management.