The results are shown in the maps in Figure 5. All maps show the expected patterns of concordance over time: areas where the NDVI signal is highly dynamic, such as the northern production areas, are more consistent than desert areas, where the signal is mainly audible. However, there is a big difference where each metric provides negative values: the map does not display negative values, the Watterson M metric map only takes negative values when the correlation is negative, but the map of the Ji-Gallo AC index shows huge areas of negative values throughout the territory. The comparison between b and r reflects the added value of using the former, which includes distortions that do not exist in the latter. The extent of these distortions in relation to global deviations can be interconnected in the map, while datasets compliance is displayed in the map regardless of these distortions. Through numerical analysis of different proposed metrics, this document shows that a modified version of The Mielke Index is preferable to the others. This index, cited here, is adimensional, limited, symmetrical, easy to calculate and directly interpretable in relation to the commonly used pearson coefficient of correlation r. This index can in principle be considered as a natural extension to r that regulates the downward r value depending on the distortion that occurs in the data. In contrast to previous studies, Ji-Gallo9 has explicitly developed an index that would meet the symmetry criteria. This index, proposed for the comparison of remote sensing images, is defined as follows: Figure 4 shows a geometric representation of the difference between these non-cryptic squares and the total squares calculated in relation to the 1:1 line. Figure 4 also shows geometrically how these non-cryptic squares in the surface differ from what is proposed by Willmott6 and Ji-Gallo9 using the formulations of the equation (19) and (20).

iii) Legates and McCabe Index (ELM) (Legates and McCabe 1999) “abs” are absolute values (the difference between the observed and simulated value). In theory, the value of PMARE ranges from 0% to ∞ (positive infinity). The interpretation and characterization of the index will be discussed at a later date. Among the statistical indices, some quantify the difference in the emission of observed or experimental measurement models, while others focus on the correlation between model forecasts and measurements. Essentially, Fox (1981) recommended calculating and reporting the following four types of differences: average error, average absolute error, variance in the distribution of difference, and defects of the root ailment square (or its square – the average quadratic error). These difference-based statistics quantify the output output of the measurement model. Specific indicators are also proposed. Bellocchi et al. (2002) proposed an expert system to calculate a composite indicator of solar radiation performance assessment. They used a correlation coefficient (r), a relative average value error (RRMS), modeling efficiency (EF) and student probability t to form an aggregated form. Confalonieri et al.