209 / 2024-02-27 22:03:48

A review of hyperspectral inversion methods for water quality

hyperspectral remote sensing; water quality testing; Water quality inversion

With the increase of population and the rapid development of society, the demand for water resources is also increasing. An increase in water pollution can worsen water quality and threaten human health, water ecosystem balance, economic development and social prosperity. Therefore, there is a need for continuous and accurate monitoring of water resources management. Conventional water quality monitoring methods are costly, time-consuming and labor-intensive; Remote sensing technology provides an effective way for long-term and rapid monitoring of water environment, and at the same time, large-scale observation also makes up for the shortcomings of observation samples, and the gradual maturity of hyperspectral remote sensing technology provides a good technical means for the monitoring of the whole water area, which can realize large-scale water quality control. This new method is to collect hyperspectral data of water bodies, synchronize field data sampling, and establish a water quality inversion model by analyzing the relationship between water quality parameter concentration and spectral characteristics, so as to achieve the purpose of monitoring a wide range of waters. This non-contact monitoring method can not only improve the monitoring efficiency, but also realize the monitoring of a large range of waters, provide more comprehensive data support for water quality protection and water resource management, and then realize the comprehensive monitoring and evaluation of water bodies.

At present, based on the spectral characteristics of water bodies, scholars at home and abroad have successfully used various methods to invert the quality of water bodies. In this paper, the spectral characteristics of water and the extraction of spectral characteristic bands are introduced, the main methods of estimating water quality using hyperspectral remote sensing technology are summarized, the main factors affecting the accuracy of the model are discussed, and finally the shortcomings of the model inversion process and the future research direction are prospected.