Spatial modeling and RS technique have become one of the means in managing soil properties to their evaluation and monitoring. Thus, reflected on the accuracy of the decision-making for the managing of farm systems. The study directs the use of Partial least squares regression (PLSR) as an indicator of soil parameters that relate to the reflection spectral data. The topsoil samples (0-35cm) were analyzed for ECe, CaCO3 and SOM. Soil spectral data were collected in lab conditions using an ASD spectrophotometer. These were correlated for each soil parameter and the PLSR model was applied to the calibration data (70% of the complete data), also to the verification data (the remainder 30%) to estimate the spatial prediction for each soil parameter. The results obtained that the studied parameters ranged from low to medium predictable while ECe (R2 < 0.50 and RPD < 1.40), while SOM and CaCO3 (R2 > 0.50 and RPD > 1.40). The validation R2 of ECe, SOM and CaCO3 were 0.42, 0.51 and 0.66, respectively. The RPD values were 1.10, 1.49 and 1.60 for ECe, SOM and CaCO3, respectively. The RMSE for these parameters was 1.01dS m-1 for ECe, 2.43% for SOM and 0.16% for CaCO3. It can be concluded that the integration of Vis-NIR spectra and the multivariate regression model is urgent for estimating and exploring soil parameters. These tools are studied for their accuracy in exploring soil properties. Hence, more studies need to be documented. Keeping in mind, laboratory analyzes of soil characteristics are the basis of spatial evaluation.