3 of 21 resolution, which included a cropland class. Xiong et al. (2017) produced a nominal 30-meter cropland extent map of continental Africa by using Sentinel-2 and Landsat 8 data (GFSAD30AFCE). From an African context, this product represents a major improvement for food and water security assessments and is a first step for exploring not only cropland extent but also crop type, intensity, and change. The European Space Agency released a prototype land-cover 20-meter map of Africa for 2016 with cropland as one of its classes.<sup>1</sup> However, these products still present some limitations that hamper the extraction of information on land-use dynamics for specific regions of Africa. Cropland systems across the continent are highly diverse and are often adapted to very specific environmental conditions. The phenological signatures of the different land-use types in a region can be very similar (Xiong et al. 2017). As a consequence, mapping croplands at the continental level requires large, up-to-date training and validating data sets (Xiong et al. 2017). Although high-resolution imagery and crowdsourcing (Fritz et al. 2015) are gaining ground as sources of training and data validation, these remain scarce in remote regions, constraining the precision of supervised learning algorithms. Consequently, continental and global medium-resolution products that implement a single mapping approach are not always flexible enough to accurately characterize land-use processes in specific regions of the continent.

Northern Nigeria presents a concrete example of a data-poor region where land-use information is crucial for human development. Poverty indicators over the last decade show a growing north-south divide (World Bank 2016). Although poverty rates are only 16 percent in the southern part of the country, an estimated 50.2 percent of the population lives below the poverty line in northern Nigeria, where up to 70 percent of households rely primarily on agriculture (World Bank 2016). Ongoing land-cover change and land-use processes compromise households’ main livelihood strategies, reduce their adaptation alternatives, and thus increase their vulnerability. Yet the lack of consistent and reliable land-use information hinders the accurate characterization of northern Nigeria’s agricultural sector. As the links between climate change, crop failures, poverty, migration, and conflict become more explicit in the research literature, it becomes urgent to monitor land-use processes in the region to inform decision makers and design and implement efficient policy interventions in agricultural development and poverty alleviation.

This work proposes a mapping approach specifically designed for the agricultural systems of the Sudan-Sahel region. It aims to overcome some of the limitations in the region of the global and continental remote sensing products. The approach makes extensive use of expert knowledge and exploits dense stacks of Landsat 8 imagery to capture inter- and intra-annual vegetation dynamics and better characterize the study region’s main land-use types. The proposed mapping framework is flexible and robust enough to operate with limited imagery, and can be easily and rapidly updated in successive years. We have applied this approach to produce a 30-meter spatial resolution LCLU map that emphasizes agricultural classes. This work aims to fill a gap of information and provide a precise, up-to-date assessment of agricultural systems in northern Nigeria.

> 1 For more information, see the European Space Agency’s “CCI Land Cover—S2 Prototype Land Cover 20m Map of Africa 2016,” http://2016africalandcover20m.esrin.esa.int.