## 1 Introduction  
The United Nations in 2015 agreed on 17 Sustainable Development Goals (SDGs) with the aim of ensuring peace and prosperity for the people and the planet [1]. The SDG number 2--end hunger, achieve food security and improve nutrition, and promote sustainable agriculture--aims to address this global crisis. Smallholder farming is one of the vehicles that can be used to achieve this goal [2]. Smallholder farms are in most cases the only main source of reasonable income and food security for rural livelihoods in most developing countries. To achieve this goal, spatial agricultural information such as the spatial distribution of smallholder farms and production area estimates are pre-requisites. The production area estimates provide a quantitative measure in which food security can be forecasted in rural communities. Local governments can alleviate starvation and provide targeted relief efforts by using this information. Food security in developing countries remains a big challenge that the world is currently facing [3, 4]. In Africa, smallholder farmers produce 80% of the maize in the regions, which forms part of the staple diet [5]. The smallholder maize farmers of Africa are faced with environmental problems such as insufficient rainfall because of drought, insect pest infestations, and infertile soils due to a multitude of reasons (e.g., monoculture, desertification, salinization, and degradation) [6; 7; 8]. Additionally, economic issues such as the use of outdated technologies, limited market opportunities, and limited access to capital are prevalent in smallholder farms [5; 9]. These issues coupled with an increase in demand for maize products have contributed to food insecurity, particularly in rural communities that are reliant on maize [10]. Remote sensing data offers opportunities to monitor and map smallholder farms because they are able to capture their heterogonous and complex characteristics [11]. Optical remote sensing has been used to map agricultural fields [12; 13]. However, clouds and cloud shadows remain a big challenge in extracting phenological parameters of crops during the growth stages and mapping crop fields using a multi-temporal approach due to data gaps [14]. Radar data have emerged as one of the best remote sensing tools that can be used to map agricultural crops without being affected by clouds [15]. Previously, this data type was limited to specific regions and campaigns [16]. The Sentinel-1A/B Synthetic Aperture Radar (SAR) C-band satellites were launched by the European Space Agency (ESA) with a wider coverage [17]. Applications of SAR data in agricultural crop mapping have increased over the years; this was mainly driven by free access to the data and improved spatial (10 m) and temporal (global coverage) resolutions. The smallholder farms are generally less than 2 ha in size, which makes it difficult to map them with coarse resolution sensors [18]. Therefore, the characteristics of Sentinel-1 sensors make it a suitable tool for agricultural applications [19]. Different authors have used a Sentinel-1 multi-temporal approach to map agricultural crops. [PERSON] and [PERSON] [20] used Sentinel-1 data to map smallholder maize and wheat farms in Zimbabwe. The authors used model-level data fusion (i.e., data were stacked and used as input into the models) and achieved overall accuracies of 99% and 95% for different study area sites. [PERSON] et al. [21] applied a Dynamic Conditional Random Fields (DCRFs) classification procedure on multi-temporal Sentinel-1 images to map different kinds of crops (maize, potato, sugar beet, wheat, and other classes). The authors were able to map maize with a producer and user accuracies of 93.74% and 90.04%, respectively. [PERSON] and [PERSON] [22] used comprehensive Sentinel-1 multi-temporal data to identify agricultural land cover types.