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 demand for calculating and mapping water yield is increasing for inaccessible locations or areas of conflict to support decision makers. Integrated Valuation of Environmental Services and Tradeoffs (InVEST) was applied to simulate basin hydrology. InVEST is becoming popular in the water modeling community due to its low requirements for input information, level of skill and model setup is available to the public domain. Estimation and mapping of water production, evapotranspiration and precipitation of the Nile River Basin have been performed by using open access data. This study utilized climate, soil and land use related data to model the key components of the water balance in the study region. Maps of the key parts of water balance were also produced. The spatial patterns of precipitation, actual evapotranspiration and water yield show sharp decline from south to northern part of the study basin while actual evapotranspiration fraction happens to the opposite. Our analysis confirms the ability of the InVEST water yield model to estimate water production capacity of a different part of a basin without flow meters.

The construction of large-scale water reservoir facilities in transboundary river basins always arouses intense concern and controversy. The Grand Ethiopian Renaissance Dam (GERD) under construction in Ethiopia is perceived to affect water security in Egypt and Sudan. Therefore, this study investigated the water and sediment balance of the Blue Nile River (BNR) basin and identified the spatio-temporal variation in sediment and water yields along with the construction of GERD using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) sediment and water yield models. The BNR basin experienced increasing water and sediment yields between 1992 and 2020 and has shown a growth trend since 2020. The lion’s share of water and sediment yields come from upstream of the GERD. Taken together, these results imply that the construction of the GERD will serve as a water storage and silt trap for Sudan and Egypt.