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.

In recent years, Africa has seen much construction of large-scale hydrological infrastructures in the arid and semi-arid regions of numerous countries. This paper aims to quantify the effects of this form of hydrological infrastructure, especially the Upper Atbara and Setit Dam Complex (UASDC) in Eastern Sudan, on the land use/cover (LUC) and socioeconomic domains. This paper attempts to advance our understanding of this phenomenon by using multiple approaches. A framework using the integration of 3S technologies and a logical approach for quantifying the significance of the results to society has been developed. The method used Landsat5 TM in 2002, Sentinel2A in 2018, and statistical data to create the LUC map. The final map included seven classes; the overall accuracy of changes in LUC patterns was 94.9% in 2002 and 93% in the results reveal that significant changes occurred in terms of LUC, having a considerable effect on socio-economic development. The results were analyzed with the logical approach for overall objectives, where 85% represents S1, 3.3% represents S2, and 11.7% represents S3, respectively. This study provides an insight into further investigations of the dam’s effect on climate and groundwater, and offers a new perspective on land use prediction, simulation, and environmental sustainability.