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# **Introduction**

<mark>Over the last 30 years, earth observation remote sensing for the monitoring of land use and agricultural development has grown significantly. Remote sensing-based cropland monitoring is rapidly becoming operational in different parts of the world (Gumma et al. 2014; Ozdogan 2010; Ozdogan and Gutman 2008; Thenkabail and Wu 2012). A number of agricultural monitoring systems started forecasting crop yields and production. But the advances have been geographically uneven, and the Sudan-Sahel region of Sub-Saharan Africa is one of them. Beginning with Tucker et al. (1985), a number of initiatives have studied land-cover and land-use (LCLU) trends in the region using Earth observation data (Brink and Eva 2009; CILSS 2016, 219; Fensholt et al. 2013; Herrmann, Anyamba, and Tucker 2005; Knauer et al. 2014; Spiekermann, Brandt, and Samimi 2015). But the use of remote sensing monitoring systems in the region has been sluggish. Availability and quality of land-use data have therefore been limited due to limited agricultural production, technical challenges, and slow adoption of remote sensing monitoring systems.</mark> Despite its relevance for human development and poverty alleviation, there is a lack of basic information on the distribution of cultivated land and the main land processes in the Sudan-Sahel. The region is expected to experience major changes in the near future, and land-use information remains crucial because agriculture represents the main source of livelihoods. The region’s fertility rates are among the highest in the developing world (Bertoni et al. 2016), and rural population growth will accelerate the expansion of cultivated land for subsistence farming. Likewise, the steady growth of the urban population will fuel the demand for agricultural products, which is a major driver of land-use change (DeFries et al. 2010). The consequences of climate change will likely disrupt regional agricultural practices and production. Expected temperature increases by 2050 will shorten crop-growing cycles, leading to severe yield reductions and threatening food production systems (Cervigni, Valentini, and Santini 2013; Jones and Thornton 2003; Porter et al. 2014). Coping strategies for the changing conditions may lead to progressive land degradation, further compromising rural livelihoods and increasing their vulnerability to future internal and external shocks (Leichenko and O'Brien 2008). For instance, as productivity decreases due to climate change, farmers may overexploit soil or expand cultivated lands into more marginal lands.

Global remote sensing–based LCLU change products include coarse-resolution land-use information for the Sudan-Sahel region (for example, MODIS MCS12Q1 and GlobCover 2005 and 2009). These products have been integrated with national statistics to generate a more robust outcome (Ramankutty et al. 2008). However, although coarse spatial resolution data meet the observational requirements for the world’s large agricultural regions, they are not well suited for monitoring crops in regions with highly heterogeneous agricultural landscapes dominated by smaller farms, such as those in the Sudan-Sahel region. Furthermore, the static nature of these products fails to capture changes over time (Leroux et al. 2014; Pittman et al. 2010). The opening of the Landsat archive in 2008–09 (Woodcock et al. 2008) and, more recently, the launch of new medium-resolution sensors (Landsat 8, Sentinel-2 Disaster Monitoring Constellation, and so forth) offers unprecedented opportunities to study LCLU change at higher spatial resolution. Several initiatives are already exploiting these new data with increasing processing capabilities to map agricultural landscapes with higher spatial detail at scales more relevant to African agricultural processes. For example, Gong et al. (2013) created a global LCLU map at 30-meter spatial