Cropland Field Monitoring: MMV Page 1 Montana Cropland Enrolled Farm Fields Carbon Sequestration Field Sampling, Measurement, Monitoring, and Verification: Application of Visible-Near Infrared Diffuse Reflectance Spectroscopy (VNIR) and Laser-induced Breakdown Spectroscopy (LIBS) [report]

Lee Spangler, Ross Bricklemyer, David Brown
2012 unpublished
Brown. 2010. On-the-go VisNIR: Potential and limitations for mapping soil clay and organic carbon. Comput. Electron. Agric. 70:209-216. Abstract 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
more » ... ed 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 (350-2224 nm, 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 9 local samples included in the calibrations, standard error of prediction (SEP) values were 2.6 and 3.4 g kg -1 for lab and on-the-go VisNIR respectively, with  SOC = 3.2 g kg -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 = 53.1 g kg -1 and residual product differential (RPD) = 1.8 with on-the-go SEP = 69.4 g kg -1 and RPD = 1.4. With more variability ( Clay = 91.4 g kg -1 and CV = 49.6%), both lab and on-the-go VisNIR show better explanatory power. There are a number of potential explanations for degraded onthe-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. 2.1
doi:10.2172/1037876 fatcat:sferywieorbqvpoafgx6pbngce