Predicting soil organic carbon content using simulated insitu spectra and moisture correction algorithms in southern Xinjiang, China

Abstract

Soil organic carbon (SOC) is an essential component of the terrestrial carbon pool, and changes in SOC are critical to ecosystem stability and agricultural production. In-situ field spectroscopy, such as visible and near-infrared (vis-NIR) spectroscopy, has proven to be an ideal tool to achieve rapid and efficient detection of SOC. However, soil moisture interference is one of the main challenges for the measurement of SOC using in-situ spectroscopy. Great progress has been made in using external parameter orthogonalization (EPO), direct standardization (DS) and piecewise direct standardization (PDS) to remove the influence of moisture on using vis-NIR spectroscopy to predict SOC. However, most current studies were conducted based on a certain moisture, which makes it difficult to provide a comprehensive guide for SOC detection using in situ spectroscopy. In this study, 135 surface (0–20 cm) soil samples were collected from the Aksu region of Xinjiang, northwestern China. The soil samples were wetted to a saturated moisture content state after manual removal of salts. The spectra data of 11 different moisture levels during natural air-drying of the wet soil samples were measured to analyze and compare the performance of three algorithms, EPO, DS and PDS, in removing the effect of moisture and improving the accuracy of SOC prediction. The results show that PDS has a relatively poor ability to remove the effect of moisture among the three moisture correction algorithms. When the moisture content of the soil exceeds 48%, EPO, DS and PDS cannot effectively remove the disturbance of moisture. The EPO algorithm was the most effective in removing moisture effects when the soil moisture content (SMC) was 25–48%. Using EPO, the predicted R2 and RPIQ of SOC increased by >0.19 and 0.87, respectively. The DS algorithm was the best method to remove the moisture effect on soil vis-NIR spectra when the SMC was 6–25%, and the R2 and RPIQ predictions of SOC after removing the effect of moisture by DS increased by >0.09 and 0.87, respectively. However, when the moisture content is <6%, the influence of soil moisture on the prediction accuracy of SOC spectra is negligible, and SOC can be predicted with high accuracy using in-situ spectra without the introduction of moisture correction algorithms. An appropriate moisture correction algorithm should be selected to remove moisture interference according to the SMC condition.