TY - JOUR
T1 - 3D spectral analysis in the VNIR–SWIR spectral region as a tool for soil classification
AU - Ogen, Yaron
AU - Goldshleger, Naftaly
AU - Ben-Dor, Eyal
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/9/15
Y1 - 2017/9/15
N2 - Visible, near-infrared and shortwave-infrared (VNIR–SWIR) spectroscopy has proven to be an efficient, rapid and low-cost method for soil spectral analysis that can improve on the results obtained from today's traditional methods of conducting soil surveys. Nonetheless, this tool is used mostly in the laboratory and at surface level. The main objective of this paper is to develop a new optical method for characterizing soil profiles, towards improving the efficiency and accuracy of the traditional soil survey. We used airborne hyperspectral data from the AisaFENIX sensor for surface classification and ASD spectral measurements of soil samples for subsurface analysis. A total of 643 soil samples were extracted from 48 cores, each core representing a soil profile. All samples were air-dried, crushed and sieved, and then analyzed by ASD spectrometer under laboratory conditions. Clay content was also measured to provide additional information. The 3D spectral data were analyzed using SAM algorithm, spectral gradient (m), k-means clustering and gley horizon parameter (G) to classify soils and distinguish between soil horizons in each core. The results suggest that these parameters can provide satisfactory results both from laboratory measurements and hyperspectral remote sensing data (R2 = 0.81 for clay content and R2 = 0.78 for gleying conditions) in order to distinguish between the soil horizons using 3D spectral information. Moreover, the method is satisfactory for obtaining soil types from 3D spectral sensing as well as evaluating the catena development and other spatial soil distributions.
AB - Visible, near-infrared and shortwave-infrared (VNIR–SWIR) spectroscopy has proven to be an efficient, rapid and low-cost method for soil spectral analysis that can improve on the results obtained from today's traditional methods of conducting soil surveys. Nonetheless, this tool is used mostly in the laboratory and at surface level. The main objective of this paper is to develop a new optical method for characterizing soil profiles, towards improving the efficiency and accuracy of the traditional soil survey. We used airborne hyperspectral data from the AisaFENIX sensor for surface classification and ASD spectral measurements of soil samples for subsurface analysis. A total of 643 soil samples were extracted from 48 cores, each core representing a soil profile. All samples were air-dried, crushed and sieved, and then analyzed by ASD spectrometer under laboratory conditions. Clay content was also measured to provide additional information. The 3D spectral data were analyzed using SAM algorithm, spectral gradient (m), k-means clustering and gley horizon parameter (G) to classify soils and distinguish between soil horizons in each core. The results suggest that these parameters can provide satisfactory results both from laboratory measurements and hyperspectral remote sensing data (R2 = 0.81 for clay content and R2 = 0.78 for gleying conditions) in order to distinguish between the soil horizons using 3D spectral information. Moreover, the method is satisfactory for obtaining soil types from 3D spectral sensing as well as evaluating the catena development and other spatial soil distributions.
KW - Clay content
KW - Gleying
KW - Hyper-depth spectral classifier (HSC)
KW - Soil classification
KW - Spectroscopy
KW - VNIR–SWIR
UR - http://www.scopus.com/inward/record.url?scp=85019832533&partnerID=8YFLogxK
U2 - 10.1016/j.geoderma.2017.04.020
DO - 10.1016/j.geoderma.2017.04.020
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AN - SCOPUS:85019832533
SN - 0016-7061
VL - 302
SP - 100
EP - 110
JO - Geoderma
JF - Geoderma
ER -