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Open AccessArticle

Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction

1
TECLIM, George Lemaitre Center for Earth and Climate, Earth and Life Institute, Université Catholique de Louvain, 1348 Louvain-La-Neuve, Belgium
2
Sustainable Agriculture Sciences Department, Rothamsted Research, Harpenden, Herts AL5 2JQ, UK
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(7), 1889; https://doi.org/10.3390/su11071889
Received: 20 February 2019 / Revised: 18 March 2019 / Accepted: 25 March 2019 / Published: 29 March 2019
(This article belongs to the Section Sustainable Agriculture)
Laboratory spectroscopy has proved its reliability for estimating soil organic carbon (SOC) by exploiting the relationship between electromagnetic radiation and key spectral features of organic carbon located in the VIS-NIR-SWIR (350–2500 nm) region. While this approach provides SOC estimates at specific sampling points, geo-statistical or interpolation techniques are required to infer continuous spatial information. UAS-based proximal or remote sensing has the potential to provide detailed and spatially explicit spectral sampling of the topsoil at the field or even watershed scale. However, the factors affecting the quality of spectral acquisition under outdoor conditions need to be considered. In this study, we investigate the capabilities of two portable hyperspectral sensors (STS-VIS and STS-NIR), and two small-form multispectral cameras with narrow bands in the VIS-NIR region (Parrot Sequoia and Mini-MCA6), to predict SOC content. We collected spectral data under both controlled laboratory and outdoor conditions, with the latter being affected by variable illumination and atmospheric conditions and sensor-sample distance. We also analysed the transferability of the prediction models between different measurement setups by aligning spectra acquired under different conditions (laboratory and outdoor) or by different instruments. Our results indicate that UAS-compatible small-form sensors can be used to reliably estimate SOC. The results show that: (i) the best performance for SOC estimation under outdoor conditions was obtained using the VIS-NIR range, while the addition of the SWIR region decreased the prediction accuracy; (ii) prediction models using only the narrow bands of multispectral cameras gave similar or better performances than those using continuous spectra from the STS hyperspectral sensors; and (iii) when used in outdoor conditions, the micro hyperspectral sensors substantially benefitted from a laboratory model calibration followed by a spectral transfer using an internal soil standard. Based on this analysis, we recommend VIS-NIR portable instruments for estimating spatially distributed SOC data. The integration of these sensors in UAS-mapping devices could represent a cost-effective solution for soil research and precision farming applications when high resolution data are required. View Full-Text
Keywords: soil organic carbon; proximal sensing; hyperspectral sensors; multispectral sensors; precision agriculture soil organic carbon; proximal sensing; hyperspectral sensors; multispectral sensors; precision agriculture
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MDPI and ACS Style

Crucil, G.; Castaldi, F.; Aldana-Jague, E.; van Wesemael, B.; Macdonald, A.; Van Oost, K. Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction. Sustainability 2019, 11, 1889. https://doi.org/10.3390/su11071889

AMA Style

Crucil G, Castaldi F, Aldana-Jague E, van Wesemael B, Macdonald A, Van Oost K. Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction. Sustainability. 2019; 11(7):1889. https://doi.org/10.3390/su11071889

Chicago/Turabian Style

Crucil, Giacomo; Castaldi, Fabio; Aldana-Jague, Emilien; van Wesemael, Bas; Macdonald, Andy; Van Oost, Kristof. 2019. "Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction" Sustainability 11, no. 7: 1889. https://doi.org/10.3390/su11071889

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