Soil spectroscopy can provide low-cost and high-density data for predicting various soil properties. However, a relatively weak correlation between the spectra and the measurements of salinized soil makes spectroscopy difficult to use in predicting areas at risk of salinization, especially for low and moderately saline soils. The main objective of the study was to propose an effective approach based on Vis-NIR spectroscopy and geostatistics for mapping soil salinity in the Neretva River valley (Croatia). A spectral index (SI), which synthesizes most of salt affected soil properties, was defined and used as covariate in ordinary cokriging (COK) for improving electrical conductivity (ECe) prediction. The proposed approach was compared with a univariate predictor (ordinary kriging, OK), which uses only ECe data and a multivariate predictor (ordinary cokriging, COK) using more covariates such as some chemical properties of primary importance in salt affected soils (Ca2+, Mg2+, Na+, SO4 2−, Cl− concentrations and pH). The study was carried out in an agricultural area (5068 ha) located in the Neretva River valley (Croatia). Topsoil (0–30 cm) samples were collected at 246 locations with a grid (500m×500 m) sampling scheme and analyzed for some chemical and physical properties. Moreover, soil samples were used for visible and near infrared (Vis-NIR) spectra measurements with a range of wavelength between 350 and 2500 nm. The spectral data were pre-processed and ECe was predicted with partial least squares regression (PLSR). The first significant latent variable, which accounted for 85% of the total variance, was selected and used as a SI to quantify and map spatial variation of soil salinity. The univariate and multivariate geostatistical approaches provided results and performances quite similar. Regarding the two multivariate approaches, the one using only the spectral index as covariate has provided better results in terms of unbiasedness and accuracy. Moreover, the spectral index SI is also very cost-effective and it could then be used in possible broad-scale surveys for preventing soil salinity at landscape scale.

A geostatistical Vis-NIR spectroscopy index to assess the incipient soil salinization in the Neretva River valley, Croatia

Colombo, Claudio
Supervision
;
Di Iorio, Erika
Formal Analysis
;
2018-01-01

Abstract

Soil spectroscopy can provide low-cost and high-density data for predicting various soil properties. However, a relatively weak correlation between the spectra and the measurements of salinized soil makes spectroscopy difficult to use in predicting areas at risk of salinization, especially for low and moderately saline soils. The main objective of the study was to propose an effective approach based on Vis-NIR spectroscopy and geostatistics for mapping soil salinity in the Neretva River valley (Croatia). A spectral index (SI), which synthesizes most of salt affected soil properties, was defined and used as covariate in ordinary cokriging (COK) for improving electrical conductivity (ECe) prediction. The proposed approach was compared with a univariate predictor (ordinary kriging, OK), which uses only ECe data and a multivariate predictor (ordinary cokriging, COK) using more covariates such as some chemical properties of primary importance in salt affected soils (Ca2+, Mg2+, Na+, SO4 2−, Cl− concentrations and pH). The study was carried out in an agricultural area (5068 ha) located in the Neretva River valley (Croatia). Topsoil (0–30 cm) samples were collected at 246 locations with a grid (500m×500 m) sampling scheme and analyzed for some chemical and physical properties. Moreover, soil samples were used for visible and near infrared (Vis-NIR) spectra measurements with a range of wavelength between 350 and 2500 nm. The spectral data were pre-processed and ECe was predicted with partial least squares regression (PLSR). The first significant latent variable, which accounted for 85% of the total variance, was selected and used as a SI to quantify and map spatial variation of soil salinity. The univariate and multivariate geostatistical approaches provided results and performances quite similar. Regarding the two multivariate approaches, the one using only the spectral index as covariate has provided better results in terms of unbiasedness and accuracy. Moreover, the spectral index SI is also very cost-effective and it could then be used in possible broad-scale surveys for preventing soil salinity at landscape scale.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/79937
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