  Computing the agreement between two continuous sequences is of great interest
in statistics when comparing two instruments or one instrument with a gold
standard. The probability of agreement (PA) quantifies the similarity between
two variables of interest, and it is useful for accounting what constitutes a
practically important difference. In this article we introduce a generalization
of the PA for the treatment of spatial variables. Our proposal makes the PA
dependent on the spatial lag. As a consequence, for isotropic stationary and
nonstationary spatial processes, the conditions for which the PA decays as a
function of the distance lag are established. Estimation is addressed through a
first-order approximation that guarantees the asymptotic normality of the
sample version of the PA. The sensitivity of the PA is studied for finite
sample size, with respect to the covariance parameters. The new method is
described and illustrated with real data involving autumnal changes in the
green chromatic coordinate (Gcc), an index of "greenness" that captures the
phenological stage of tree leaves, is associated with carbon flux from
ecosystems, and is estimated from repeated images of forest canopies.
