Penetrometer-Mounted VisNIR Spectroscopy: Implementation and Algorithm Development for In-Situ Soil Property Predictions

Many applications in agriculture and environmental sciences rely on high-quality spatially explicit soils data. Due to the costs of sample collection, preparation, and laboratory analysis, traditional techniques for collection of new soils data are often expensive. In this study, we developed the framework for a novel soil measurement technique; a penetrometer-mounted visible near infrared (VisNIR) spectrometer. The penetrometer-mounted VisNIR probe is capable of measuring soil properties of in situ soils at high-depth resolutions (i.e. 5-cm vertical spacing). A fully functional in situ VisNIR probe could reduce the cost of soil measurement by supplementing or replacing traditional soil measurement techniques. For in situ VisNIR to be a viable tool, in situ VisNIR needs to be compatible with existing spectral modeling techniques designed for spectra collected from air-dried and ground soils in the laboratory. One issue with in situ VisNIR is that, unlike spectra collected under laboratory conditions, in situ spectra are altered by in situ effects (e.g soil moisture, structure, field temperatures, etc.) and therefore are incompatible with existing laboratory approaches. Using soils from central Texas, we tested two methods for mitigating in situ effects; direct standardization (DS) and external parameter orthogonalization (EPO). Our tests indicate that EPO was more effective than DS. We further tested EPO on tropical soils from Brazil. The EPO performed well on these soils demonstrating that EPO can be applied to a wide variety of soil types. Finally, we tested the EPO on in situ spectra collected using the penetrometer-mounted VisNIR probe and again, the EPO performed satisfactorily. By iii coupling the EPO with a penetrometer-mounted VisNIR probe we have demonstrated the viability of in stiu VisNIR. The penetrometer-mounted system can utilize existing laboratory-based spectral modeling tools for prediction of soil properties at high-depth-resolutions and is a viable tool for rapid, cost effective soil measurement

Using a VNIR Spectral Library to Model Soil Carbon and Total Nitrogen Content

n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression (SVR), to predict soil carbon and nitrogen contents for the Rapid Carbon Assessment (RaCA) project. The second objective was to investigate whether VNIR modeling accuracy can be improved by sample stratification. The third objective was to evaluate the usefulness of these calibrated models to predict external soil samples. The final objective was devised to compare four calibration transfer techniques: Direct Standardization (DS), Piecewise Direct Standardization (PDS), External Parameter Orthogonalization (EPO) and spiking, to transfer field sample scans to laboratory scans of dry ground samples. Results showed that non-linear modeling techniques (ANN, RF and SVR) significantly outperform linear modeling technique (PLS) for all soil properties investigated (accuracy of PLS \u3c RF \u3c SVR ≤ ANN). Local models developed using the four auxiliary variables (Region, land use/land cover class, master horizon and textural class) improved the prediction for all properties (especially for PLS models) compared to the global models (in terms of Root Mean Squared Error of Prediction) with master horizon models outperforming other local models. From the calibration transfer study, it was evident that all the calibration transfer techniques (except for DS) can correct for spectral influences caused by sample intactness. EPO and spiking coupled with ANN model calibration showed the highest performance in accounting for the intactness of samples. These findings will be helpful for future efforts in linking legacy spectra to field spectra for successful implementation of the VNIR sensor systems for vertical or horizontal soil characterization. Advisor Yufeng G

Using a VNIR Spectral Library to Model Soil Carbon and Total Nitrogen Content

n-situ soil sensor systems based on visible and near infrared spectroscopy is not yet been effectively used due to inadequate studies to utilize legacy spectral libraries under the field conditions. The performance of such systems is significantly affected by spectral discrepancies created by sample intactness and library differences. In this study, four objectives were devised to obtain directives to address these issues. The first objective was to calibrate and evaluate VNIR models statistically and computationally (i.e. computing resource requirement), using four modeling techniques namely: Partial least squares regression (PLS), Artificial neural networks (ANN), Random forests (RF) and Support vector regression (SVR), to predict soil carbon and nitrogen contents for the Rapid Carbon Assessment (RaCA) project. The second objective was to investigate whether VNIR modeling accuracy can be improved by sample stratification. The third objective was to evaluate the usefulness of these calibrated models to predict external soil samples. The final objective was devised to compare four calibration transfer techniques: Direct Standardization (DS), Piecewise Direct Standardization (PDS), External Parameter Orthogonalization (EPO) and spiking, to transfer field sample scans to laboratory scans of dry ground samples. Results showed that non-linear modeling techniques (ANN, RF and SVR) significantly outperform linear modeling technique (PLS) for all soil properties investigated (accuracy of PLS \u3c RF \u3c SVR ≤ ANN). Local models developed using the four auxiliary variables (Region, land use/land cover class, master horizon and textural class) improved the prediction for all properties (especially for PLS models) compared to the global models (in terms of Root Mean Squared Error of Prediction) with master horizon models outperforming other local models. From the calibration transfer study, it was evident that all the calibration transfer techniques (except for DS) can correct for spectral influences caused by sample intactness. EPO and spiking coupled with ANN model calibration showed the highest performance in accounting for the intactness of samples. These findings will be helpful for future efforts in linking legacy spectra to field spectra for successful implementation of the VNIR sensor systems for vertical or horizontal soil characterization. Advisor Yufeng G