## 5 Discussion
Smallholder farms normally practice crop mixing, un-equal row planting patterns, and lack of irrigation systems. These practices can influence the backscatter intensity from maize. Scattering from nearby vegetation, such as grass and soil-canopy multiple scattering, can also contribute towards misclassification. We showed that the PCA data reduction method can be used to facilitate the mapping of smallholder maize farms. Machine learning algorithms require data that can be separable to successfully classify data into their respective classes ([PERSON] et al., 2016). The PCA provides this by decorrelating the multi-temporal backscatter values into components that describe unique information for different classes, therefore enhancing the probability of accurate classification. Maize can grow up to an average height of 2 m and the structural volume of the crop also increases as the leaves also grow. This makes it possible to map maize with radar data, since the VV and VH polarizations are sensitive to vegetation structure and volumetric changes ([PERSON] et al., 2016). A frequent revisit of Sentinel-1 of 10-12 days and its high spatial resolution of 10 meters can capture the phenological stages of maize ([PERSON] et al., 2016). The increase in backscatter intensity for the maize class makes it possible to map smallholder farms in complex environments. PCA also suppresses other classes with low variable backscatter over time; these classes include grasslands and bare soil in our study area. The PCA image composites provide clear examples, where the advantage of the first stage of image fusion used in this study can be seen (Figure 6). The high level of importance of VH and VV polarizations were expected. Other studies, such as [PERSON] et al. [PERSON] et al. (2016), illustrated that the VH, VV, and VH/VV polarizations ranked differently depending on the type of crop that was investigated. VH polarization was more suitable for rice and rapeseed discrimination, VV polarization was more suitable for alfalfa, and the VH/VV ratio was suitable for discriminating crops from different seasons. The results can be used to generate spatial agricultural information such as estimating crop production areas and their spatial distributions in areas where survey datasets are not available, such as in our study. The results can be used to inform local government about the levels of agricultural activities in rural communities, thus providing ways to forecast food shortages and improve food security. The use of Sentinel-1 multi-temporal data provides an opportunity to afford this critical information regardless of the environmental conditions such as clouds or lack of extensive reference data. These results can also be used to contribute towards the SDG number 2. We therefore recommend the use of Sentinel-1 multi-temporal data to map smallholder farms at a provincial scale. More studies need to be done to explore the phase and amplitude data extracted from the backscatter intensities and their contribution to the accuracy of classifying smallholder farms. Different image fusion techniques and multi-sensor data fusion should also be explored. The limitation of this study was that there were no agricultural statistics to independently validate the areas obtained by the machine learning algorithms. These validation data are normally collected by local agricultural departments. For example, the United States Department of Agriculture (USDA) uses remote sensing and extensive reference data provided by the National Agricultural Statistics Service (NASS) to generate the crop layer and associated statistics ([PERSON] et al., 2016). In areas with limited reference data, such as smallholder farms in developing countries, remote sensing technology provides a sustainable way to generate agricultural statistics with reasonable accuracies [18; 20]. Processing multi-temporal data requires computational resources that are otherwise not easily accessible in developing countries.