The multiple uses of land-cover models have led to validation with choice metrics or an ad
hoc choice of the validation metrics available. To address this, we have identified the major
dimensions of land-cover maps that ought to be evaluated and devised a Similarity
Validation (SimiVal) tool. SimiVal uses a linear regression to test a modelled projection
against benchmark cases of, perfect, observed and systematic-bias, calculated by rescaling
the metrics from a random case relative to the observed, perfect case. The most informative
regression coefficients, p-value and slope, are plot on a ternary graph of ‘similarity space’
whose extremes are the three benchmark cases. This plot provides a rigorous similarity
assessment against these extremes and other projections. SimiVal is tested on projections
of two deliberately contrasting land-cover models to show the similarity between intra- and
inter-model parameterisations. Predictive and exploratory models can benefit from the tool.