Mielke7.17 proposed another definition of the μ denominator, based on a non-parametric approach using random permutations. In this case, the baseline consists of the sum of the differences between each point and each other point. Such an index can be expressed in general for different values γ as such: figures of the indexes of the sf agreement and b, discussed in this document, on data sets with different correlation and systematic additive or multiplier distortions. The first case of study is satellite measurements of the Standardized Difference Vegetation Index (NDVI) obtained from October 1, 2013 to May 31, 2014 on Northwest Africa. The spatial resolution is 1 km and the temporal resolution is a decade (a decade is a period that results from the division of each calendar month into 3 parts, which can take values of 8, 9, 10 or 11 days). The data are obtained from two different instruments on two different satellite platforms: SPOT-VEGETATION and PROBA-V (these are called VT and PV for simplicity). PV data is available through the copernicus Global Land Service Portal24, while VT archive data is provided courtesy of the GFC MARSOP25 project. Although the geometric and spectral characteristics of satellites and data processing chains have been as close as possible, differences between products are still expected because the instruments are not identical. The aim here is to quantify where the time series do not coincide in the region. Since there is no reason to argue that one should be a better reference than the other, a symmetrical match index should be applied to each pair of time series, resulting in values that can be attributed geographically. Symmetrical, i.e. it should have the same numerical value when values are switched on to and in the equation.

This is necessary because it is assumed that there is no comparison for the evaluation of the agreement. Limited between a lower limit (z.B 0) that does not correspond to an agreement and a ceiling (for example. B 1) A perfect match. One consequence is that higher values should always indicate greater agreement. Quantifying the proximity of two data sets is a common and necessary undertaking in the field of scientific research. The pearson-moment r correlation coefficient is a widespread measure of the degree of linear dependence between two sets of data, but gives no indication of the similarity of the values of these series in size. Although a number of indices have been proposed to compare a dataset to a reference, little data is available to compare two datasets with equivalent (or unknown) reliability. After a brief review and numerical testing of the metrics designed to accomplish this task, this document shows how an index proposed by Mielke can, with a minor modification, satisfy a number of desired characteristics, namely a dimensional, limited, symmetrical, easy to calculate and directly interpretable in relation to r.

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