Hyperspectral remote sensing technology for water quality monitoring: knowledge graph analysis and Frontier trend

Abstract

Water environment health assessment is one of the vital fields closely related to the quality of human life. The change of material contained in water will lead to the reflectance change of hyperspectral remote sensing data. According to this phenomenon, the water quality parameters are calculated to achieve the purpose of water quality monitoring. Series knowledge graphs in this field are drawn after analyzing 564 publications from WOS (Web of Science) and EI (The Engineering Index) databases since 1994 with the support of VOSviewer and CiteSpace. Including statistics of documents publication time, contribution analysis, the influence of publications and journals, and the influence of funding institutions. It is concluded that the research trend of hyperspectral water quality monitoring is the machine learning algorithm based on UAV (Unmanned Aerial Vehicle) hyperspectral instrument data by analyzing scientific research cooperation, keyword analysis, and research hotspots. The whole picture of the research is obtained in this field from four subfields: application scenarios, data sources, water quality parameters, and monitoring algorithms in this paper. It is summarized that the miniaturization, integration, and intelligence of hyperspectral sensors will be the research trend in the next 10 years or even longer. The conclusions have significant reference values for this field.