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

Author:
Xiaoshuai Pei, Kenneth A. Sudduth, Kristen S. Veum, Minzan Li
Source:
Sensors 2019 v.19 no.5 pp. 1011-1026
ISSN:
1424-8220
Subject:
calcium, calibration, cation exchange capacity, clay soils, decision support systems, least squares, magnesium, near-infrared spectroscopy, neural networks, penetrometers, potassium, reflectance spectroscopy, sand, sand fraction, sensors (equipment), silt fraction, silty soils, soil electrical conductivity, soil organic carbon, soil pH, soil profiles, soil texture, soil water, soil water content, total nitrogen, ultraviolet-visible spectroscopy, Missouri
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.
Agid:
6477321
Handle:
10113/6477321