*2.4. Evaluation of Precision*

In order to compare the predictive performance of various spectral parameters and methods, we used statistical indicators, such as the coefficient of determination (*R<sup>2</sup>*), root mean square error (*RMSE*), and relative error of prediction (*REP*), defined as follows:

$$\mathcal{R}^2 = \sum\_{i=1}^n (\mathcal{G}\_i - \overline{y})^2 / \sum\_{i=1}^n (y\_i - \overline{y})^2 \tag{6}$$

$$RMSE = \sqrt{\sum\_{i=1}^{n} \left( y\_i - \hat{y}\_i \right)^2 / n} \tag{7}$$

$$REP = \frac{100RMSSE}{\overline{y}}\tag{8}$$

where *yi* is the measured values, *y* is the average of the measured values, *y*ˆ*i* is the predicted value, and *n* is the number of samples. The closer the value of *R*<sup>2</sup> is to 1, the smaller *RMSE* and *REP* are, and the better the model accuracy. We applied 20-fold cross validation to assess the robustness of the estimation models.
