Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis could be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one, and we compared different approaches for sensitivity analysis that could handle the specificities of the model: a high number of input parameters and a limited size of sample due to computational cost and spatialized output. After a preliminary screening step, we calculated Sobol' indices obtained from polynomial chaos expansion, Hilbert–Schmidt independence criterion (HSIC) dependence measures and feature importance measures obtained from random forest surrogate model. Results from the different methods were compared regarding both the information they provide and their computational cost. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamic behaviour. Conclusions about the advantages and disadvantages of each method may help modellers to conduct global sensitivity analysis on other such modular and distributed hydrological models as there has been a growing interest in these approaches in recent years.