Next Article in Journal
Assessment of the Shear Strength of Pile-to-Soil Interfaces Based on Pile Surface Topography Using Laser Scanning
Previous Article in Journal
Black Phosphorus-New Nanostructured Material for Humidity Sensors: Achievements and Limitations
Previous Article in Special Issue
Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra
Article Menu
Issue 5 (March-1) cover image

Export Article

Open AccessArticle
Sensors 2019, 19(5), 1011; https://doi.org/10.3390/s19051011

Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe

1
Key Laboratory of Modern Precision Agriculture System Integration Research-Ministry of Education, China Agricultural University, Beijing 100083, China
2
USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, MO 65211, USA
*
Author to whom correspondence should be addressed.
Received: 13 December 2018 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 27 February 2019
(This article belongs to the Special Issue Proximal Soil Sensing)
  |  
PDF [1534 KB, uploaded 27 February 2019]
  |  

Abstract

Optical diffuse reflectance spectroscopy (DRS) has been used for estimating soil physical and chemical properties in the laboratory. In-situ DRS measurements offer the potential for rapid, reliable, non-destructive, and low cost measurement of soil properties in the field. In this study, conducted on two central Missouri fields in 2016, a commercial soil profile instrument, the Veris P4000, acquired visible and near-infrared (VNIR) spectra (343–2222 nm), apparent electrical conductivity (ECa), cone index (CI) penetrometer readings, and depth data, simultaneously to a 1 m depth using a vertical probe. Simultaneously, soil core samples were obtained and soil properties were measured in the laboratory. Soil properties were estimated using VNIR spectra alone and in combination with depth, ECa, and CI (DECS). Estimated soil properties included soil organic carbon (SOC), total nitrogen (TN), moisture, soil texture (clay, silt, and sand), cation exchange capacity (CEC), calcium (Ca), magnesium (Mg), potassium (K), and pH. Multiple preprocessing techniques and calibration methods were applied to the spectral data and evaluated. Calibration methods included partial least squares regression (PLSR), neural networks, regression trees, and random forests. For most soil properties, the best model performance was obtained with the combination of preprocessing with a Gaussian smoothing filter and analysis by PLSR. In addition, DECS improved estimation of silt, sand, CEC, Ca, and Mg over VNIR spectra alone; however, the improvement was more than 5% only for Ca. Finally, differences in estimation accuracy were observed between the two fields despite them having similar soils, with one field demonstrating better results for all soil properties except silt. Overall, this study demonstrates the potential for in-situ estimation of profile soil properties using a multi-sensor approach, and provides suggestions regarding the best combination of sensors, preprocessing, and modeling techniques for in-situ estimation of profile soil properties. View Full-Text
Keywords: diffuse reflectance spectroscopy; precision agriculture; profile soil properties; proximal soil sensing; in-situ sensing diffuse reflectance spectroscopy; precision agriculture; profile soil properties; proximal soil sensing; in-situ sensing
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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Pei, X.; Sudduth, K.A.; Veum, K.S.; Li, M. Improving In-Situ Estimation of Soil Profile Properties Using a Multi-Sensor Probe. Sensors 2019, 19, 1011.

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