In this case, the baseline of the μ denominator is defined on the basis of both and. However, this last index presents a number of serious defects that are illustrated in the analysis below. 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: Symmetrical, i.e. it should have the same numerical value when the values of the equation are switched and in it. This is necessary because it is assumed that there is no comparison for the evaluation of the agreement. 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. The spatial representation of the temporal correspondence between the time series from two Earth observation satellites, according to different compliance metrics described in the text, calculated and illustrated by statistical software r. (version 3.2.1, www.R-project.org/).

Willmott et al. (2011) proposed a new index, dr, and they compared the dr to “mean absolute error (MAE) ” recordings that vary logically with MAE. However, this should be compared to an average absolute relative error, as MAE may vary with different samples/data sets, while the “average absolute relative error” value may be the same (i.e., there is no change in the relative model). In this study, the dr index does not follow the logical trend within a given data set, as in Table 2 (combined analysis); and also ambiguously between different sets (1st year and data combined) – with a PMARE value. Similar inconsistencies are also observed for random records (Table 4, 1 . . . 3. Recordings – with PMARE). where both and must be extracted from the GMFR regression. Both approaches have the same mistake.

To be consistent with the definition of overall deviations, non-cryptic deviations must be calculated orthogonally in relation to the line, i.e. as if the line were line 1:1. By indicating the absolute value (the difference between the observed value and the 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. In this study, we look at different metrics that are proposed in the literature that can be used to evaluate the registration agreement. We then test and compare their performance on synthetic datasets, pointing out some of their inadequacies. We explain why a permutation index initially proposed by Mielke7 may be considered the most appropriate after a small change, as it fulfills all the desired characteristics for such an index, including that of being interpretable in terms of correlation coefficient r. We also propose a more refined approach to separately examining non-systematic and systematic contributions to differences of opinion in the data set. Finally, we apply the available metrics and the proposed index to two cases of actual comparative studies: one refers to the time series of the standardized difference vegetation index (NDVI), which was acquired during the same period by two different satellite missions, and the other refers to two chronological series of gross primary production (GPP) estimated by different modeling approaches. Legates, D.

R. – McCabe, G. J. A refined index of the model`s performance: a counter-response. International Journal of Climatology 33, 1053-1056.