A good knowledge about species traits variability in relation to their environment is the cornerstone of landscape-oriented species management studies. One way to infer this relationship is to compare species signatures in space and time from field data with spatially explicit population dynamics models outputs. However, the inference robustness relies on the available field data, and thus on the quality of the underlying sampling strategy. Field sampling is constrained by several factors, such as the number of landscape replicates, possible number of temporal sessions and number of sample locations, that need to be accounted for prior to field sampling. We set and illustrate a heuristic method to answer the question of optimal sampling conditioned by these landscape-induced constraints. First we studied a real agricultural landscape to determine its mean properties in terms of configuration and composition. The real landscape properties were used as constraints in a landscape model to generate a collection of landscapes with similar properties. On the other hand, we formulated population dynamics models (hereafter noted Process Models (PM)) carrying competing hypotheses about two ecological processes—population growth and dispersal—in relation to spatial covariates for Pterostichus melanarius, a carabid species involved in pest regulation. We simulated these spatially explicit models and extracted their sampling-dependent signatures, i.e. metrics computed on different population samples. We defined a sampling design quality as its ability to capture the contrasts between the PM signatures, summarised by the performance of a classification procedure. The most relevant sampling design was selected on the basis of classification performance and in situ feasibility. Finally we explored the effects of the a priori ecological hypotheses quality on classification performances, through a sensitivity analysis of the PM parameters. While some improvements remain to be achieved before being fully operational for landscape ecologists, our framework contributes to bringing closer sampling theory and its application on the field. It endorses the use of landscape modelling to design sampling prior to field experiment to bring out the best from sampled data.