For many environmental applications, an accurate spatial mapping of land cover is a major concern. Currently, land cover products derived from satellite data are expected to offer a fast and inexpensive way of mapping large areas. However, the quality of these products may also largely depend on the area under study. As a result, it is common that various products disagree with each other, and the assessment of their respective quality still relies on ground validation datasets. Recently, crowdsourced data have been suggested as an alternate source of information that might help overcome this problem. However, crowdsourced data still remain largely discarded in scientific studies due to their inherent poor quality assurance. The aim of this paper is to present an efficient methodology that allows the user to code information brought by crowdsourced data even if no prior quality estimation is at hand and possibly to fuse this information with existing land cover products in order to improve their accuracy. It is first suggested that information brought by volunteers can be coded as a set of inequality constraints about the probabilities of the various land use classes at the visited places. This in turn allows estimating optimal probabilities based on a maximum entropy principle and to proceed afterwards with a spatial interpolation of these volunteers’ information. Finally, a Bayesian data fusion approach can be used for fusing multiple volunteers’ contributions with a remotely-sensed land cover product. This methodology is illustrated in this paper by focusing on the mapping of croplands in Ethiopia, where the aim is to improve the mapping of cropland as coming out from a land cover product with mitigated performances. It is shown how crowdsourced information can seriously improve the quality of the final product. The corresponding results also suggest that a prior assessing of remotely-sensed data quality can seriously improve the benefit of crowdsourcing campaigns, so that both sources of information need to be accounted together in order to optimize the sampling efforts.
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