Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially |
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Authors: | A Volkan Bilgili Fevzi Akbas Harold M van Es |
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Institution: | (1) Department of Soil Science, Agriculture Faculty, Harran University, Sanliurfa, 63300, Turkey;(2) Department of Soil Science, Agriculture Faculty, Gaziosmanpasa University, Tokat, 60100, Turkey;(3) Department of Crop and Soil Science, Cornell University, Ithaca, NY 14853-1901, USA |
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Abstract: | Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision
soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches
for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m)
in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable
cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining
data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from
first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction.
The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents,
(R
2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1,
37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either
the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR
was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied
to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy
of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions
of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial
prediction of soil properties and enables a reduction in sampling and laboratory analyses. |
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