Abstract:

As a non-destructive, fast, low-cost technique with the minimum preparation of samples and no risk for ecosystem, visible near infra-red spectroscopy may be replaced with methods using in vitro. The current research aims to assess reflective spectroscopy in estimation of properties of soils locating ion farming plains at Tehran, Khuzestan and Yazd provinces. To this end, 88 soil samples were collected from the studied zones and their basic properties were calibrated using standard techniques in vitro. The spectral analysis of soils was done using land spectroscopic device at wavelengths (240-400 nm).The types of preprocessing techniques were assessed after recording of spectra and PCA and PLSR models were utilized to determine main properties of soil. The best method was used for estimation of regressive functions to predict studied parameters after linear regression. The findings showed that both PCA and PLSR models had high precision for determination of parameters of soil properties and they could interpret high variances of soil properties and PLSR model was more precise than PCA model. With respect to RPD statistic the best estimation of the offered regressive functions was calculated for minerals (RPD=9.34), pH (RPD=4.45), and nitrogen (RPD>2) each of these three factors were classified in series-A and the lower estimations were computed for clay, silt, gravel, quantities of phosphorus, potassium, calcium, and magnesium, and gypsum within the range of (RPD=0.01-0.28). These numbers denote reasonable precision of spectral regressive functions in prediction of studied basic properties. Overall, results of this study indicate that both PCA and PLSR models have appropriate precision in determination of main parameters of soil properties and also the soil spectral data may be utilized as an indirect technique for estimation of soil physical and chemical properties and in comparison with laboratory methods this technique is more economical to determine chemical and physical properties in terms of time and cost-effectiveness with higher precision.