The importance of Remote Sensing and GIS into malaria studies has been widely demonstrated. Most of the studies that integrate remote sensing are analysing the link between environmental factors and potential mosquito's nidus presence, but focusing only on some few meteorological variables. Nevertheless, the complexity and biological dynamism of malaria transmission require the integration of multiple ecological variables. In this study remotely sensed based environmental variables have been used to build a comparative model. All these data have been integrated into a GIS, layered with other ground data sources of climate and epidemiological origin. Since the final survey is concerned with only four villages, the general purpose was to use the environmental variables derived from remote sensing as malaria transmission risks predictors for the whole study area. Another challenge was to avoid predictors' thematic redundancy. The last challenge was to avoid spatial autocorrelation since we were dealing with high spatial resolution data. For these purposes the intelligence between Remote Sensing and GIS tools is of a focal utility.