Assessing Soil Organic Carbon Content in the Peruvian Central Andes Using Visible Near Infrared (VNIR) and Mid Infrared (MIR) Spectroscopy.

Estimating soil organic carbon (SOC) content in data-poor or poorly accessible areas, such as the Andean region, is challenging due to the lack of recent soil data and the wide range of coexistent ecosystems. Furthermore, conventional methods to determine SOC are usually time consuming and expensive to use in large-scale studies, hindering the possibility to have an accurate SOC assessment in the region.

Proximal soil sensing techniques, such as visible near infrared (VNIR) and mid infrared (MIR) spectroscopy, have proven to be useful as an alternative to conventional methods for characterizing SOC but have not been tested in Andean soils.

 

The present study evaluated the potential of using VNIR and MIR spectroscopy to predict SOC content in the Central Andean region, using multivariate methods. Three study areas were identified across the Peruvian Central Andes. A total of 400 topsoil samples (0-30 cm) were collected and analyzed for SOC. The VNIR and MIR reflectance of the soil samples was measured in the laboratory.

Three modeling approaches: Partial least squares regression (PLSR), random forest (RF) and support vector machine (SVM) were used to predict SOC from VNIR and MIR spectra in the three study areas. The data was preprocessed in order to minimize the noise and optimize the accuracy of predictions. The models, for each study area, were assessed using 10-fold cross validation. Independent validation was implemented in the whole dataset (400 observations) by splitting it into calibration (70 %) and validation (30%) sets.

Overall, the results indicated potential for both VNIR and MIR spectra to predict SOC content in the Andean soils. SOC content predictions from MIR spectra markedly outperformed those from VNIR spectra.

These results suggest that VNIR and MIR spectroscopy are promising approaches for assessing SOC content in the Peruvian Central Andes.