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Remote Sens. 2017, 9(11), 1103; doi:10.3390/rs9111103

Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms

1
Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, 04103 Leipzig, Germany
2
Soil Science, University of Trier, 54286 Trier, Germany
3
Thünen Institute of Biodiversity, 38116 Braunschweig, Germany
4
Department of Environmental Chemistry, University of Kassel, 37213 Witzenhausen, Germany
*
Author to whom correspondence should be addressed.
Received: 23 August 2017 / Revised: 3 October 2017 / Accepted: 24 October 2017 / Published: 29 October 2017
(This article belongs to the Special Issue Recent Progress and Developments in Imaging Spectroscopy)
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Abstract

We explored the potentials of both non-imaging laboratory and airborne imaging spectroscopy to assess arable soil quality indicators. We focused on microbial biomass-C (MBC) and hot water-extractable C (HWEC), complemented by organic carbon (OC) and nitrogen (N) as well-studied spectrally active parameters. The aggregation of different spectral variable selection strategies was used to analyze benefits for reachable estimation accuracies and to explore spectral predictive mechanisms for MBC and HWEC. With selected variables, quantification accuracies improved markedly for MBC (laboratory: RPD = 2.32 instead of 1.33 with full spectra; airborne: 2.35 instead of 1.80) and OC (laboratory: RPD = 3.08 instead of 2.36; airborne: 2.20 instead of 1.94). Patterns of selected variables indicated similarities between HWEC and OC, but significant differences between all other soil variables. This agreed to our results of indirect approaches in which both (i) wet-chemical data of OC and N and (ii) spectra fitted to measured OC and N values were used to estimate MBC and HWEC. Compared to these approaches, we found marked benefits of laboratory and airborne data for a direct spectral quantification of MBC (but not for HWEC). This suggests specificity of spectra for MBC, usable for the determination of this important soil parameter. View Full-Text
Keywords: hyperspectral remote sensing; imaging spectroscopy; soil organic carbon; microbial biomass carbon; multivariate calibration; spectral variable selection hyperspectral remote sensing; imaging spectroscopy; soil organic carbon; microbial biomass carbon; multivariate calibration; spectral variable selection
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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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Vohland, M.; Ludwig, M.; Thiele-Bruhn, S.; Ludwig, B. Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms. Remote Sens. 2017, 9, 1103.

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