Using visible-near infrared spectroscopy to estimate whole-profile soil organic carbon and its fractions

Soil organic carbon (SOC) is crucial for soil health and quality, and its sequestration has been suggested as a natural solution to climate change. Accurate and cost-efficient determination of SOC and its functional fractions is essential for effective SOC management. Visible near-infrared spectroscopy (vis-NIR) has emerged as a cost-efficient approach. However, its ability to predict whole-profile SOC content and its fractions has rarely been assessed. Here, we measured SOC and its two functional fractions, particulate (POC) and mineral-associated organic carbon (MAOC), down to a depth of 200 ​cm in seven sequential layers across 183 dryland cropping fields in northwest, southwest, and south China. Then, vis-NIR spectra of the soil samples were collected to train a machine learning model (partial least squares regression) to predict SOC, POC, MAOC, and the ratio of MAOC to SOC (MAOC/SOC – an index of carbon vulnerability). We found that the accuracy of the model indicated by the determination coefficient of validation (R_val²) is 0.39, 0.30, 0.49, and 0.48 for SOC, POC, MAOC, and MAOC/SOC, respectively. Incorporating mean annual temperature improved model performance, and R_val² was increased to 0.64, 0.31, 0.63, and 0.51 for the four carbon variables, respectively. Further incorporating SOC into the model increased R_val² to 0.82, 0.64, and 0.59, respectively. These results suggest that combining vis-NIR spectroscopy with readily-available climate data and total SOC measurements enables fast and accurate estimation of whole-profile POC and MAOC across diverse environmental conditions, facilitating reliable prediction of whole-profile SOC dynamics over large spatial extents.