On-the-go VisNIR: Potential and limitations for mapping soil clay and organic carbon
In situ or on-the-go visible and near infrared (VisNIR) diffuse reflectance spectroscopy has been proposed as a rapid and inexpensive tool for intensively mapping soil texture and organic carbon (SOC). While lab-based VisNIR has been established as a viable technique for estimating various soil properties, few experiments have compared the predictive accuracy of on-the-go and lab-based VisNIR. In this study, eight north central Montana wheat fields were intensively interrogated using on-the-go and lab-based VisNIR. The on-the-go VisNIR system employed a spectrophotometer (3502224nm, 8-nm spectral resolution) built into an agricultural shank mounted on a toolbar and pulled behind a tractor. Regional (whole-field out cross-validation) and hybrid (regional model including randomly chosen local calibration samples) spectral models were calibrated using partial least squares regression. Lab-based spectral data consistently provided more accurate predictions than on-the-go data. However, neither in situ nor lab-based spectroscopy yielded even semi-quantitative SOC predictions. For hybrid models with nine local samples included in the calibrations, standard error of prediction (SEP) values were 2.6 and 3.4gkg?1 for lab and on-the-go VisNIR respectively, with ?SOC=3.2gkg?1. With an SOC coefficient of variation (CV)=26.7%, even with a relatively low SEP values, there was little SOC variability to explain. For clay content, hybrid-7 calibrations yielded lab SEP=53.1gkg?1 and residual product differential (RPD)=1.8 with on-the-go SEP=69.4gkg?1 and RPD=1.4. With more variability (?clay=91.4gkg?1 and CV=49.6%), both lab and on-the-go VisNIR show better explanatory power. There a number of potential explanations for degraded on-the-go predictive accuracy: soil heterogeneity, field moisture, consistent sample presentation, and a difference between the spatial support of on-the-go measurements and soil samples collected for laboratory analyses. In terms of predictive accuracy, our results are largely consistent with those previously published by Christy (2008), but on-the-go VisNIR was not able to capture the subtle SOC variability in Montana soils. Though the current configuration of the Veris on-the-go VisNIR system allows for rapid field scanning, on-the-go soil processing (i.e. drying, crushing, and sieving) could improve predictions.
on-the-go clay partial least squares regression visible and near infrared spectroscopy soil organic carbon independent validation hybrid calibration models