Poverty maps allow assessing the well-being of rural population in a spatial context and identifying poverty hotspots. The maps can be used for regional policy analysis as they help in identifying areas where the rural poor live and where rural poverty is determined by the endowment and quality of natural resources and by population pressure. Natural resource endowment was assessed in the study by calculating an Agricultural Resource Index based on the availability of different major agricultural resources. Income per-capita was calculated by using census data, adjusted by the rural population density. The results show that the better income areas of Syria are located in the irrigated or higher-rainfall areas, but lower-income pockets exist due to the presence of various ecological and topographic factors. The study provides the elements of a 'top-down' approach for poverty mapping to disaggregate income from census sources to the pixel level based on agroecological data. It is an important advancement on methodologies to link micro and macro economic analysis to successfully map poverty in data scarce regions. However, we also critically suggest a number of practical improvements for the approach. Poverty mapping can indeed become more effective and cost-efficient if classical bottom-up approaches, based on household survey data are integrated with top-down approaches such as the one presented.