## 1 Introduction  
The Reduced Emissions from Deforestation and Degradation (REDD+) framework from the United Nations Framework Convention on Climate Change (UNFCCC) is key to addressing climate change and other related environmental emergencies, such as deforestation and forest degradation [1]. REDD+ activities have triggered the need for National Forest Monitoring Systems (NFMS) that produce high-quality, reliable data on forests and forest carbon stocks. The UNFCCC encourages NFMS to use a combination of remote sensing and ground-based forest carbon inventories in order to estimate the greenhouse gas emissions that are associated with forest ecosystems [2, 3, 4]. In the last ten years, the remote sensing community has witnessed many efforts to produce accurate maps of tree cover and tree cover change with increasing spatial and temporal resolutions. While global initiatives to map tropical forest change do exist [5, 6, 7], national institutions often look for methods over which they can have complete governance. The current quality, quantity and free availability of remote sensing data is helping to achieve such a goal. In Africa, however, alternative methods are still needed in order to overcome certain limitations and challenges, such as specific technical capacities and skills, computing power and data availability [8]. In the case of East Africa, those limitations add to the already existing challenge of mapping and assessing the tree cover in the dry domain [9, 10]. The monitoring of tropical dense moist and boreal forests by optical remote sensing platforms is largely operational [5; 6; 11; 12]; while, in contrast, mapping dry forests with satellite imaginary presents several challenges [10; 13]. As illustrated in Figure 1, these challenges include the deciduous nature of the dry forests, the high variation in the structure and canopy cover over time and across biomes as well as a wide range of factors that may distort their aspect as viewed from remote sensing platforms. As opposed to evergreen forests where the canopy tends to be fully closed, dry forests have a full range of canopy coverage levels, from closed to open. Furthermore, the structure of dry forests is also varied, with the tree cover in open forests (or woodlands) being mixed in different proportions with herbaceous and shrub formations [14; 15]. Regarding phenology, dry forests present a mix of phenological characteristics, from semi-deciduous where only a certain proportion of the trees simultaneously loses their leaves through to fully deciduous [16]. During the dry season many of these forests are subject to fires, generally passing through the understory, with burn scars making a major impact on the forest's level of reflectance. Generally, dry forests are under high anthropogenic pressure from fuel wood collection, charcoal production and grazing, all of which change the structure and composition of the forest canopy and understory [17]. In consequence, dry forests' reflectance and backscatter, as measured from remote sensing platforms, vary both in time and in space, making the consistent monitoring and discrimination of these dry forests from other land-cover types very challenging, especially through the use of optical data, given the low number of cloud-free acquisitions. Most cloud-free optical satellite images are available in the dry season, a period when these forests have lost their leaves, making them difficult to discriminate from an understory grass layer. Conversely, during the wet season it may be difficult to distinguish a tree layer from shrubs. Despite collecting imagery in all cloud conditions, space-borne synthetic aperture radar (SAR) data have only been used on an ad-hoc basis for forest monitoring until recently, with a test case investigating SAR data's use in dry tropical forests of Bolivia [18] and another in the humid tropical forests of Central Africa [19].