Soil carbon mapping using on-the-go near infrared spectroscopy, topography and aerial photographs

Efficient tools for accurate soil carbon (SC) mapping are imperative for large scale assessment of total SC stocks and their changes in time as well as for site-specific tailoring of agricultural management practices. On-the-go near infrared (NIR) reflectance spectroscopy has been used recently in aid to the conventional, laborious and expensive soil analyses, since NIR measurements are taken in-situ quickly and non-destructively. However, NIR spectrum data need to be effectively calibrated with conventionally measured SC. Our objectives are to compare calibration approaches, including pre-processing transformations (Savitzky–Golay derivatives, standard normal variate and mean centering) and multivariate statistical methods (principal component regression, partial least squares, partial least squares leaving one-outlier-out) for using NIR spectra data as SC predictor, to evaluate NIR reliability in predicting SC under low carbon contents typical for Midwest Alfisols; and finally to compare predictions of SC by using three sources of auxiliary information (NIR spectral data, visible-NIR reflectance obtained from aerial photographs and topographical features). No improvements in calibration accuracy were observed when using pre-processing transformations. Partial least squares (RMSE = 1.90) tended to perform better than principal component regression (RMSE = 1.96); especially when spectral-NIR outliers are dropped (RMSE = 1.68). Our results suggested that visible-NIR data from aerial photographs used along with topographical attributes outperformed on-the-go spectral NIR data. Topographical data improved prediction in the models with aerial photograph visible-NIR data; however no improvement was noticed when used with spectral-NIR data. Though, NIR spectral data is frequently used as a proxy for SC prediction, we notice that this auxiliary information is not well suited under all scenarios. Particularly, when SC levels are low and the range of SC data is narrow, as in this study, NIR was only moderately successful in predicting SC.

► Pre-processing of NIR data did not improved carbon prediction accuracy in sandy soils.
► Topographical features improved SC prediction accuracy in aerial data.
► Aerial data produced higher prediction accuracy than NIR in independent test data sets.
► Aerial photography with topography is preferable to spectral NIR for SC prediction in sandy soils.