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.

Water plays a vital role in sustaining the natural functioning of the entire ecosystem that supports life on Earth. It plays key roles in the well‐being of society in numerous ways. However, climate variability and land use land cover (LULC) change have caused spatiotemporal water supply variation. Disentangling the effects of climate variability from LULC change on water supply is crucial for sustainable water resource management. The main purpose of this study is, therefore, to disentangle the relative contribution of LULC change and climate variability to the overall average annual water supply variation. Residual trends analysis combined with Integrated Valuation of Environmental Services and Tradeoffs (InVEST) annual water yield model was adopted to perform simulations and disentangle the relative impacts of climate variability and LULC change. Ground and satellite data were used in this study. The study area has experienced a significant increasing wetness trend and significant LULC dynamics between 2003 and 2017. As a result, an increasing water supply was observed due to the joint effects of climate variability and LULC change in the watershed (203 mm). The contribution of climate variability was 94%, whereas LULC contributes only 6% from 2003 to 2017. Climate variability negatively led to water supply variation while LULC change contributed positively from 2010 to 2017. Although the ongoing soil and water conservation (SWC) practices improved vegetation cover and water retention of the watershed, climate variability is the main driver of water supply variation. Therefore, SWC practices should incorporate ecosystem‐based climate change adaptation strategies and scale up to community‐based integrated watershed management to sustain water supply.

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.

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.