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Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada

1
Department of Geography, Environment and Geomatics, University of Guelph, Guelph, ON N1G 2W1, Canada
2
Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
3
School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
4
Institute of Geography, University of Erlangen-Nuremberg, 91058 Erlangen, Germany
5
Institute of Geographical Sciences, Free University Berlin, 12249 Berlin, Germany
6
Department of Geography, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1298; https://doi.org/10.3390/rs11111298
Received: 23 April 2019 / Revised: 22 May 2019 / Accepted: 22 May 2019 / Published: 31 May 2019
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
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Abstract

The recent use of hyperspectral remote sensing imagery has introduced new opportunities for soil organic carbon (SOC) assessment and monitoring. These data enable monitoring of a wide variety of soil properties but pose important methodological challenges. Highly correlated hyperspectral spectral bands can affect the prediction and accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful to describing SOC. This study evaluates the efficiency of visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data to identify the most informative hyperspectral bands responding to SOC content in agricultural soils. Soil samples (111) were collected over an agricultural field in southern Ontario, Canada and analyzed against two hyperspectral datasets: An airborne Nano-Hyperspec imaging sensor with 270 bands (400–1000 nm) and a laboratory hyperspectral dataset (ASD FieldSpec 3) along the 1000–2500 nm range (NIR-SWIR). In parallel, a multimethod modeling approach consisting of random forest, support vector machine, and partial least squares regression models was used to conduct band selections and to assess the validity of the selected bands. The multimethod model resulted in a selection of optimal band or regions over the VNIR and SWIR sensitive to SOC and potentially for mapping. The bands that achieved the highest respective importance values were 711–715, 727, 986–998, and 433–435 nm regions (VNIR); and 2365–2373, 2481–2500, and 2198–2206 nm (NIR-SWIR). Some of these bands are in agreement with the absorption features of SOC reported in the literature, whereas others have not been reported before. Ultimately, the selection of optimal band and regions is of importance for quantification of agricultural SOC and would provide a new framework for creating optimized SOC-specific sensors. View Full-Text
Keywords: remote sensing; agricultural soils; imaging spectroscopy; airborne hyperspectral imaging; unmanned aerial vehicle (UAV); hyperspectral; feature selection; multimethod modeling approach remote sensing; agricultural soils; imaging spectroscopy; airborne hyperspectral imaging; unmanned aerial vehicle (UAV); hyperspectral; feature selection; multimethod modeling approach
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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).
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Laamrani, A.; Berg, A.A.; Voroney, P.; Feilhauer, H.; Blackburn, L.; March, M.; Dao, P.D.; He, Y.; Martin, R.C. Ensemble Identification of Spectral Bands Related to Soil Organic Carbon Levels over an Agricultural Field in Southern Ontario, Canada. Remote Sens. 2019, 11, 1298.

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