## 5 Discussion  
This study used Sentinel-1 multi-temporal datasets to map smallholder maize farm spatial distribution and to estimate maize production area for the maize crop. A two-staged image fusion technique was employed. The first stage involved using a pixel-based PCA technique to transform the original multi-temporal backscatter values into three component images that explained more than 70% of the information. This was done for the VV, VH, and VV/VH polarizations. The second stage involved model-level fusion, where all the components were used as input features into the machine learning algorithms. The SVM and Xgboost algorithms were used as classifiers to map the distribution of the maize farms and production area in Makhuduthamaga of Limpopo province, South Africa. This study found that Sentinel-1 had a high capacity to map smallholder maize planted areas with the application of machine learning algorithms. Furthermore, the two processing strategies used in this study detected smallholder maize farms with acceptable accuracy. The accuracy assessment results were also expected. The overall accuracies were better than 90%, the cross-validation scores were greater than 85%, and the kappa coefficient of agreement and conditional kappa coefficient of agreement were all better than 90%. These results confirm the suitability of our approach in mapping smallholder farms using Sentinel-1 multi-temporal datasets. Other studies such as [PERSON] et al. (2016) used Sentinel-1 multi-temporal data to map agricultural crops by applying a Deep Recurrent Neural Network and obtained favorable results that were better than 85% in accuracy. The SVM and Xgboost algorithms estimated maize production areas to be 7073.558 \(\pm\) 0.01 ha and 7303.32 \(\pm\) 0.180 ha, respectively. These values are relatively comparable to each other and SVM seems to have smaller error margins at a 95% confidence level and slightly higher overall accuracy than the Xgboost. However, for cross-validation scores, the Xgboost performed better. [PERSON]'s test showed that the results from the two algorithms were statistically different from each other. Other authors have evaluated different machine learning algorithms and obtained mixed performance indicators. [PERSON] et al. (2016) used different ensemble classifiers (Random Forest, SVM, and Majority Voting) to map smallholder farming systems based on the cloud-based multi-temporal approach and obtained overall accuracies ranging from 60% to 72%. [PERSON] et al. (2018) used Xgboost algorithms together with Decision Tree, Random Forest, and SVM to map land cover using Gaofen-3 Polarimetric SAR (PolSAR) data and obtained overall accuracies ranging from 88.4% to 93%. [PERSON] et al. (2018) used machine learning algorithms and Deep Neural Network algorithms to map crop types and found that a Convolutional Neural  
Figure 8: Comparison of the field measured areas (y) to those generated by the classification models (x) applying the SVM and Xgboost algorithms. Network model achieved 85.5%, while the Xgboost achieved 82.4% in overall accuracy under a multi-temporal classification scenario. Overall, the results produced by the classification algorithms compared favorably with the ground-based measured areas. Both algorithms had an agreement of more than 80%. There are a few factors that may have contributed to the mapping errors as produced by the two algorithms and the radar data. Examples of these include, but are not limited to, loss of information during the PCA data reduction stage, backscatter mixing, and different planting patterns. Dimensional reduction may have contributed to the mapping errors. However, the three components that were kept for each polarization at more than 70% proved sufficient in our study. According to [PERSON] (1966), backscatter intensity is sensitive to variations in scattering geometry, distribution of scatterer size, surface reflectivity beneath the canopy, leaf area density, row structure, and orientation relative to the range domain of the radar.