Reservoirs are vital sources of water for agriculture, industry, and domestic uses. However, in the Ethiopian highlands, reservoir sedimentation is a growing challenge for dam operators as huge amounts of sediments accumulate behind dams, reducing storage capacity annually. Soil erosion worsens the problem by depleting fertile topsoil upstream and depositing sediment in downstream reservoirs, affecting rainfed and irrigated agriculture. This paper reviews sedimentation studies in reservoirs in the Ethiopian highlands and offers valuable insights into sedimentation thresholds for current and future reservoirs. Findings showed substantial sedimentation rates ranged from 4.7 to 16.2% a⁻¹ for decommissioned small reservoirs and from 1.7% to 3.6% a⁻¹ for functioning reservoirs. Moreover, annual sedimentation rates of 2.66% and 1.06% were, respectively, found for Angereb and Koga large-scale reservoirs. This indicated that sediment volume impoundment ranged between 66,489 and 30,309 m³/year for small reservoirs and 140,714 and 858,333 m³/year. This showed that the specific sediment volume was in the range of 2820–9171 m³/km²/year for small reservoirs while the value was between 2082 and 3902 m³/km²/year for large-scale reservoirs. It showed that the contribution of large catchment to reservoir area ratio and catchment shape indices was significant among other factors. The economic implications of losing reservoirs in the region was significant in that constructing new reservoirs with the same capacity as the abandoned ones ($ 1.3 million construction cost), would require approximately $18.5 million. Additionally, other direct and indirect costs, such as pollution, impacts on livelihoods, catchment area degradation, loss of reservoir area, and the decline of aquatic ecosystems, would have significant consequences in abandoning the reservoirs.

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improved resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. The research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring.

Water hyacinth (Eichhornia crassipes), native to South America, is a highly invasive aquatic plant threatening freshwater ecosystems worldwide. Its rapid proliferation negatively impacts water quality, biodiversity, and navigation. Remote sensing offers an effective means to monitor such aquatic environments by providing extensive spatial and temporal coverage with improving resolution. This systematic review examines remote sensing applications for monitoring water hyacinth and water quality in studies published from 2014 to 2024. Seventy-eight peer-reviewed articles were selected from the Web of Science, Scopus, and Google Scholar following strict criteria. Research spans 25 countries across five continents, focusing mainly on lakes (61.5%), rivers (21%), and wetlands (10.3%). Approximately 49% of studies addressed water quality, 42% focused on water hyacinth, and 9% covered both. The Sentinel-2 Multispectral Instrument (MSI) was the most used sensor (35%), followed by the Landsat 8 Operational Land Imager (OLI) (26%). Multi-sensor fusion, especially combining Sentinel-2 MSI with Unmanned Aerial Vehicles (UAVs), was frequently applied to enhance monitoring capabilities. Detection accuracies ranged from 74% to 98% using statistical, machine learning, and deep learning techniques. Key challenges include limited ground-truth data and inadequate atmospheric correction. The integration of high-resolution sensors with advanced analytics shows strong promise for effective inland water monitoring.