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

Author： Paz-Kagan Tarin Zaady Eli Salbach Christoph Schmidt Andreas Lausch Angela Zacharias Steffen Notesco Gila Ben-Dor Eyal Karnieli Arnon

Publisher： MDPI

E-ISSN： 2072-4292|7|11|15748-15781

ISSN： 2072-4292

Source： Remote Sensing, Vol.7, Iss.11, 2015-11, pp. : 15748-15781

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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.