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

Vohland, Michael; Ludwig, Marie; Thiele-Bruhn, Sören; Ludwig, Bernard

doi:10.3390/rs9111103

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

^1,*

^2,3,

and

⁴

Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, 04103 Leipzig, Germany

Soil Science, University of Trier, 54286 Trier, Germany

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.

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

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)

View Full-Text Download PDF

Browse Figures

Citation Export

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

▼ Show Figures

Graphical abstract

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

Share and Cite

MDPI and ACS Style

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. https://doi.org/10.3390/rs9111103

AMA Style

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 Sensing. 2017; 9(11):1103. https://doi.org/10.3390/rs9111103

Chicago/Turabian Style

Vohland, Michael, Marie Ludwig, Sören Thiele-Bruhn, and Bernard Ludwig. 2017. "Quantification of Soil Properties with Hyperspectral Data: Selecting Spectral Variables with Different Methods to Improve Accuracies and Analyze Prediction Mechanisms" Remote Sensing 9, no. 11: 1103. https://doi.org/10.3390/rs9111103

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI