There is a huge problem of soil pollution throughout the world. Its results lead to thrashing of environment and health. So it is important to know about soil quality with metals. This research aims to identify content of copper within soil samples using FieldSpec4Spectroradiometer (Analytical Spectral Devices, Inc., USA). The instrument ASD FieldSpec4 Spectroradiometer is used for gathering spectral signature of soil samples collected from different agricultural lands in Aurangabad district of Maharashtra state in India. We used Partial Least Squares Regression (PLSR) to calculate the expected reflectance spectroscopy in the VNIR ranges to identify the copper content in the soil samples. It is used with several spectral preprocessing techniques including first derivative and Savitzky-Golay smoothing, Absorbance, Standard Normal Variate and Continuum Removal. Then, the expected results were evaluated by relative root mean square error (RRMSE) and coefficients of determination (R2). According to the criteria of minimal RRMSE and maximal R2, the observations using the PLSR models with the FD pretreatment was (RRMSE =0.0008- 0.1453, R2 =0.9987), SNV pretreatment was (RRMSE= 0.0004,R2 =0.9793 ), and CR pretreatment was (RRMSE =0.0003 , R2 =0.9789). Wavebands at around 650-700 nm and 900-1000 nm were selected as important spectral variables to construct final models. The correlation analyses and regression results in the PLSR models both suggest that the main mechanism for estimating Cu content in this case study lies in its correlation with Fe content. In conclusion, concentrations of copper in soils could be indirectly assessed by soil spectra, therefore, spectral reflectance would be an alternative tool for monitoring soil heavy metals contamination.
