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Cited by 57Soil Geostatistics and MappingSpectroscopy and Chemometric AnalysesRemote Sensing in Agriculture

Sensor data fusion for soil health assessment

Open access
Veum, Kristen S., Sudduth, Kenneth A., Kremer, Robert J., Kitchen, Newell R.·2017·Geoderma
DOI 10.1016/j.geoderma.2017.05.031
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Abstract

Assessment of soil health involves determining how well a soil is performing its biological, chemical, and physical functions relative to its inherent potential. Due to high cost, labor requirements, and soil disturbance, traditional laboratory analyses cannot provide high resolution soil health data. Therefore, sensor-based approaches are important to facilitate cost-effective, site-specific management for soil health. In the Central Claypan Region of Missouri, USA, visible and near-infrared (VNIR) diffuse reflectance spectroscopy has successfully been used to estimate biological components of soil health as well as Soil Management Assessment Framework (SMAF) scores. In contrast, estimation models for important chemical and physical aspects of soil health have been less successful with VNIR spectroscopy. The primary objective of this study was to apply a sensor fusion approach to estimate soil health indicators and SMAF scores using VNIR spectroscopy in conjunction with soil apparent electrical conductivity (ECa), and penetration resistance measured by cone penetrometer (i.e., cone index, CI). Soil samples were collected from two depths (0–5 and 5–15 cm) at 108 locations within a 10-ha research site encompassing different cropping systems and landscape positions. Soil health measurements and VNIR spectral data were obtained in the laboratory, while CI and ECa data were obtained in situ. Calibration models were developed with partial least squares (PLS) regression and model performance was evaluated using coefficient of determination (R2) and root mean square error (RMSE). Models integrating ECa and CI with VNIR reflectance data improved estimates of the overall SMAF score (R2 = 0.78, RMSE = 7.21%) relative to VNIR alone (R2 = 0.69, RMSE = 8.41%), reducing RMSE by 14%. Improved models were also achieved for estimates of the individual chemical, biological, and physical soil health scores, demonstrating reductions in RMSE of 2.8, 5.4, and 10.0%, respectively. The results of this study illustrate the potential for rapid quantification of soil health by fusing VNIR sensor data with auxiliary data obtained from complementary sensors.

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  • Combining laboratory-based visible and near-infrared spectroscopy with in situ measurements such as electrical conductivity and penetration resistance may be particularly useful.

    “Combining laboratory-based visible and near-infrared spectroscopy with in situ measurements such as electrical conductivity and penetration resistance may be particularly useful (Veum et al., 2017).”

    From Soil quality – A critical review (2018) · cites this paper as Veum et al., 2017

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Authors
Veum, K. S., Sudduth, K. A., Kremer, R. J., & Kitchen, N. R.
Affiliations
  • Agricultural Research Service (US)
  • University of Missouri (US)
Corresponding author
Veum, Kristen S.
ORCID
Venue
Geoderma
Publisher
Elsevier BV
Published
2017-11
Volume
305
Pages
53-61
ISSN
0016-7061 (ISSN-L)
DOI
10.1016/j.geoderma.2017.05.031
OpenAlex
W2620093133
Cited by
57
References
68
Citations by year
2026: 11 · 2025: 8 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 4 · 2020: 9 · 2019: 7 · 2018: 6
FWCI
3.37
Type
journal article
Language
en
Open access
Licence
Locations
Geoderma (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Soil Geostatistics and Mapping
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
soil health assessment, sensor fusion, visible and near-infrared spectroscopy, diffuse reflectance spectroscopy, apparent soil electrical conductivity, cone penetrometer, soil penetration resistance, Soil Management Assessment Framework, soil quality indicators, partial least squares regression, site-specific soil management

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