Next Article in Journal
3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor
Previous Article in Journal
Missing RRI Interpolation Algorithm based on Locally Weighted Partial Least Squares for Precise Heart Rate Variability Analysis
Previous Article in Special Issue
Dynamic Neural Network Modelling of Soil Moisture Content for Predictive Irrigation Scheduling
Article Menu

Export Article

Open AccessArticle
Sensors 2018, 18(11), 3869; https://doi.org/10.3390/s18113869 (registering DOI)

Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization

1
USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA
2
Department of Statistics, University of Missouri, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Received: 29 September 2018 / Revised: 26 October 2018 / Accepted: 9 November 2018 / Published: 10 November 2018
(This article belongs to the Special Issue Proximal Soil Sensing)
Full-Text   |   PDF [3076 KB, uploaded 10 November 2018]   |  

Abstract

In situ, diffuse reflectance spectroscopy (DRS) profile soil sensors have the potential to provide both rapid and high-resolution prediction of multiple soil properties for precision agriculture, soil health assessment, and other applications related to environmental protection and agronomic sustainability. However, the effects of soil moisture, other environmental factors, and artefacts of the in-field spectral data collection process often hamper the utility of in situ DRS data. Various processing and modeling techniques have been developed to overcome these challenges, including external parameter orthogonalization (EPO) transformation of the spectra. In addition, Bayesian modeling approaches may improve prediction over traditional partial least squares (PLS) regression. The objectives of this study were to predict soil organic carbon (SOC), total nitrogen (TN), and texture fractions using a large, regional dataset of in situ profile DRS spectra and compare the performance of (1) traditional PLS analysis, (2) PLS on EPO-transformed spectra (PLS-EPO), (3) PLS-EPO with the Bayesian Lasso (PLS-EPO-BL), and (4) covariate-assisted PLS-EPO-BL models. In this study, soil cores and in situ profile DRS spectrometer scans were obtained to ~1 m depth from 22 fields across Missouri and Indiana, USA. In the laboratory, soil cores were split by horizon, air-dried, and sieved (<2 mm) for a total of 708 samples. Soil properties were measured and DRS spectra were collected on these air-dried soil samples. The data were randomly split into training (n = 308), testing (n = 200), and EPO calibration (n = 200) sets, and soil textural class was used as the categorical covariate in the Bayesian models. Model performance was evaluated using the root mean square error of prediction (RMSEP). For the prediction of soil properties using a model trained on dry spectra and tested on field moist spectra, the PLS-EPO transformation dramatically improved model performance relative to PLS alone, reducing RMSEP by 66% and 53% for SOC and TN, respectively, and by 76%, 91%, and 87% for clay, silt, and sand, respectively. The addition of the Bayesian Lasso further reduced RMSEP by 4–11% across soil properties, and the categorical covariate reduced RMSEP by another 2–9%. Overall, this study illustrates the strength of the combination of EPO spectral transformation paired with Bayesian modeling techniques to overcome environmental factors and in-field data collection artefacts when using in situ DRS data, and highlights the potential for in-field DRS spectroscopy as a tool for rapid, high-resolution prediction of soil properties. View Full-Text
Keywords: Bayesian Lasso; diffuse reflectance spectroscopy; external parameter orthogonalization; partial least squares regression; profile soil properties; proximal soil sensing; soil carbon; soil texture Bayesian Lasso; diffuse reflectance spectroscopy; external parameter orthogonalization; partial least squares regression; profile soil properties; proximal soil sensing; soil carbon; soil texture
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

S. Veum, K.; A. Parker, P.; A. Sudduth, K.; H. Holan, S. Predicting Profile Soil Properties with Reflectance Spectra via Bayesian Covariate-Assisted External Parameter Orthogonalization. Sensors 2018, 18, 3869.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top