Multiple linear regression (MLR) is a regression technique that analyzes a dependent parameter (destructively measured traits) using two or more independent parameters (SRIs are listed in Table 1, which were derived from both radiometric ground-based data and QuickBird satellite imagery). MLR attempts to model the linear relationship between the independent and the response (dependent) variable [36,37,38]. In addition, MLR was used to predict three measured traits as PLSR. The best model for both Cal. and Val. was also chosen depending on the lowest value of RMSE and mean absolute deviations (MAD) as well as the highest value for R2. The least squares approach was used to calculate the parameters using the regression equation, which minimizes the sum of the errors squared. The formula of MLR is:

where, for i = n observations, Yi = dependent variable, xi = explanatory variables, β0 = y-intercept (constant term), βp = slope coefficients for each explanatory variable, ϵ = the model’s error term (also known as the residuals).

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