Next Article in Journal
An Algorithm for Burned Area Detection in the Brazilian Cerrado Using 4 µm MODIS Imagery
Next Article in Special Issue
The Effect of Epidermal Structures on Leaf Spectral Signatures of Ice Plants (Aizoaceae)
Previous Article in Journal
Evaluation of Satellite and Reanalysis Soil Moisture Products over Southwest China Using Ground-Based Measurements
Previous Article in Special Issue
Towards an Interoperable Field Spectroscopy Metadata Standard with Extended Support for Marine Specific Applications
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(11), 15748-15781; doi:10.3390/rs71115748

Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy

1
The Remote Sensing Laboratory, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel
2
Department of Natural Resources, Agricultural Research Organization, Gilat Research Center, 85280, Israel
3
Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, D-04318 Leipzig, Germany
4
Department for Monitoring and Exploration Technologies, UFZ Helmholtz Centre for Environmental Research, D-04318 Leipzig, Germany
5
Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv 69989, Israel
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 31 August 2015 / Revised: 5 November 2015 / Accepted: 9 November 2015 / Published: 23 November 2015
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
View Full-Text   |   Download PDF [1966 KB, uploaded 23 November 2015]   |  

Abstract

Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale. View Full-Text
Keywords: land-use change; imaging spectroscopy; reflectance spectroscopy; spectral soil quality index; soil quality index land-use change; imaging spectroscopy; reflectance spectroscopy; spectral soil quality index; soil quality index
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Paz-Kagan, T.; Zaady, E.; Salbach, C.; Schmidt, A.; Lausch, A.; Zacharias, S.; Notesco, G.; Ben-Dor, E.; Karnieli, A. Mapping the Spectral Soil Quality Index (SSQI) Using Airborne Imaging Spectroscopy. Remote Sens. 2015, 7, 15748-15781.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top