Cross-border malaria is a major obstacle for malaria elimination as stated by Wangdi et al. (2015). In residual transmission foci of the cross-border areas, the high spatial resolution risk mapping would help defining and/or evaluating context-dependent surveillance, control and elimination actions. High spatial resolution remote sensing (RS) permits to characterize the environment, landscape and human settlements independently of international limits. However, RS-based risk mapping of vectorborne diseases rarely specifies, in a complete and standard way, the underlying risk model. Here, we propose to explicit the different risks, their links and determinants, to better specify and justify the malaria risk estimation process and better evaluate the results significance. With the help of the actors involved in malaria surveillance and control in the region, we first built and formalized in Unified Modeling Language (UML) a systemic and holistic conceptual model of the malaria risks and their determinants. Then, spatialized and qualified indicators related to the presence probability of the main malaria vector in the region, the susceptibility of the landscape to favor human-vector exposure, and the human presence, density and activities, were combined by means of a multiplicative aggregation procedure. The conceptual model permitted to our mapping approach to be better justified, positioned and qualified. We state this approach can help the RS-based risk mapping to actually enter the public health practice, by facilitating the understanding and informed interpretation of the method and results by the health actors, especially in cross-border contexts where RS data are particularly relevant.