Objective  To establish an accurate predicted model for free iron in soil based on visible and near infrared (vis-NIR) reflectance spectroscopy, provide a simple, rapid and economical method for soil free iron determination, and facilitate the pedogenesis and classification of soil.

Method  Soil samples in B horizon were collected from eighty-two upland soil profiles in Guangxi including ferralosols, ferrosols, argosols and cambosols. Chemical and spectral properties of soil samples were analyzed under laboratory condition. The correlation between spectral reflectance after transformation and free iron content in soils was analyzed. The predicted models of soil free iron were established by the method of partial least squares regression (PLSR) and stepwise multiple linear regression (SMLR) based on characteristic bands. The optimal model was determined by evaluating the coefficient of determination (R²), root mean square error (RMSE), and residual prediction deviation(RPD).

Result  Soil spectral curves had obvious characteristics of free iron absorption and reflection peaks near 457, 800 and 900 nm bands respectively. Free iron content in soils negatively correlated with the raw spectral reflectance. The correlation coefficient between spectral reflectance and free iron content in soils increased significantly after differential transformation of the raw spectrum. The predicted model of free iron content in soils established by the first-order differential spectral transformation and SMLR based on characteristic bands of 400−580 and 760−1 300 nm had the highest accuracy, R² and RPD of the verification set were 0.85, and 2.62 respectively, and RMSE was 8.41 g·kg⁻¹.

Conclusion  It is feasible to rapidly and cost-effectively predict free iron content in soils using vis-NIR spectral technology. Soil spectral reflectance of upland in Guangxi has a high correlation with soil free iron content. SMLR is a good method to establish the predicted model of soil free iron content.