A timely and accurate crop type mapping is very significant, and a prerequisite for agricultural regions and ensuring global food security. The combination of remotely sensed optical and radar datasets presents an opportunity for acquiring crop information at relative spatial resolution and temporal resolution adequately to capture the growth profiles of various crop species. In this paper, we employed Sentinel-1A (S-1) and Sentinel-2A (S-2) data acquired between the end of June and early September 2016, on a semi-arid area in northern Nigeria. A different set of (VV and VH) SAR and optical (SI and SB) images, illustrating crop phenological development stage, were employed as inputs to the two machines learning Random Forest (RF) and Support Vector Machine (SVM) algorithms to automatically map maize fields. Significant increases in overall classification were shown when the multi-temporal spectral indices (SI) and spectral band (SB) datasets were added with the different integration of SAR datasets (i.e., VV and VH). The best overall accuracy (OA) for maize (96.93%) was derived by using RF classification algorithms with SI-SB-SAR datasets, although the SI datasets for RF and SB datasets for SVM also produced high overall maize classification accuracies, of 97.04% and 97.44%. The outcomes indicate the robustness of the RF or SVM methods to produce high-resolution maps of maize for subsequent application from agronomists, policy planners, and the government, because such information is lacking in our study area.

The study aimed to generate informative data on solar radiation in order to establish sustainable solar energy that will support domestic needs and agricultural production and processing industries in Jubek State, South Sudan. Solar radiation intensity, timely data variation, site landscape, and environment were considered. Input data used was remotely sensed data, digital elevation model, land used land cover (LULC) processed with Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS). The spatio-temporal distribution analysis results show that (62%) 11,356.7 km² of the study area is suitable for solar energy farm with an annual potential of about 6.05 × 109 GWh/year out of which only 69.0158 GW h/year is required to meet the local demand of 492,970 people residing in the study area, i.e., 0.11% (1249.2 km²) of Jubek State. Solar energy required for producing and processing 1 ton of different crop ranges between 58.39 × 10⁻⁶ and 1477.9 × 10⁻⁶ GWh and area size between 10.7 and 306.3 km², whereas 1 ton of animal production requires solar energy ranging between 750.1 × 10⁻⁶ and 8334 × 10⁻⁶ GWh and area of about 137.8 to 1531.5 km². These findings will assist in the establishment of agro-processing industries which will eventually lead to poverty reduction through job creation and improvement of food quantity and quality. The simple approach applied in this study is unique, especially for the study area, thus it can be applied to some other locations following the same steps.

Energy security is one of the challenging issues hindering developmental progress in developing countries. Wind power as a renewable energy source can play a significant role in poverty reduction if adequate information is provided. In this study, multi-approach technics were applied for a better understanding of the wind energy potential in Jubek State, South Sudan. Geographic Information System (GIS), remote sensing, and mathematical equations were applied in identifying suitable locations, potential power per unit area, wind farm layout, design of appropriate turbine size, and utilization of wind energy in both agricultural and domestic sectors. Wind speed, land use land cover, and digital elevation maps of the study area were processed in ArcGIS, MATLAB (Weibull distribution), and Minitab software. The results show that 17,331.4 km² (94.64%) of the study area is appropriate for wind power generation, with wind density of about 3.65 W/m² and installation capacity about 19,757.79 MW, resulting in an annual energy production of about 7269.29 GWh. With the proposed wind turbine, one ton of various crops and animal products require 1–4 and 2–20 turbines, respectively. Therefore, the step-by-step procedures followed in this study will contribute to poverty reduction through improving agricultural productivity and food quality.

The Republic of South Sudan lacks adequate data to support decision-makers in planning. Therefore, a land use land cover (LULC) study was conducted in Jubek State for 17 years (2000–2017). It was divided into three time intervals, using remote sensing (RS), geographic information system (GIS), Landsat TM, Landsat ETM+, and Landsat 8 OLI approaches. A transition matrix for the total change was developed to generate spatiotemporal and quantitative indicators to analyze LULC spatiotemporal dynamics for better developmental decisions. Overall accuracy assessment results were 97.41% (kappa 0.96), 90.45% (kappa 0.85), and 91.5% (kappa 0.89) for years 2000, 2009, and 2017, respectively. Furthermore, quantitative and spatiotemporal results show that built up areas drastically increase, especially from 2009 to 2017. The most dominant class in the study area was grassland, 9929.9 km² (54.22%), followed by forest, 5555 km² (30.33%), barren land, 2497.3 km² (13.64%), built up areas, 166.7 km² (0.9%), farmland, 128.31 km² (0.71%), and water bodies, 35.91 km² (0.96%). The outcomes of the analysis show that since 1955 Jubek State (Juba) has been the preferable place for the local citizens’ settlement in South Sudan. Unfortunately, agricultural production was insufficient due to the limited cultivated area; on the other hand, the study area is rich in natural resources and could meet local people’s demand if a proper strategy such as LULC transformation is well implemented.