Assessing River Water Quality Using Different Remote Sensing Technologies

Abstract

【Objective】 Remote sensing has become a prominent tool for monitoring and evaluating surface water quality across catchment and basin scales. This paper compares different remote sensing technologies for assessing water quality of terrestrial rivers. 【Method】 We took the Fuyang River in Handan City as an example. Spectral data in different seasons were acquired from three distinct remote sensing platforms: Sentinel-2 satellite images, unmanned aerial vehicles (UAVs) equipped with multispectral sensors, and ASD field spectrometers. The random forest (RF) algorithm was used to derive the inversion model from each platform to assess water quality. 【Result】 ① The results obtained from RF models for different platforms showed noticeable seasonal variation. Overall, the calculated results were most accurate in the summer, followed by spring, and were least reliable in winter. During summer, remote sensing data obtained from all three platforms can accurately estimate water quality parameters. In spring, UAV remote sensing images were sufficient. ② The RF model derived from the UAV multispectral data can effectively predict turbidity (Turb) and suspended solids (SS) of water i the river, regardless of seasons. ③ The RF model derived from the hyperspectral data was most accurate for estimating total nitrogen, with R2 >0.9 in spring. ④ Hyperspectral reflectivity curves for different seasons showed distinct variation due to the change in pollution level. In summer, reflectivity distribution curves display markedly spatial variation. The reflectivity values at each wavelength varied significantly, with pronounced changes in absorption and reflection. The reflectivity curves in spring and winter were similar, but showed pronounced changes in spring, regardless of the wavelength. 【Conclusion】 Remote sensing images obtained from different platforms offer a wealth of data for river water quality monitoring. They provide a new avenue for real-time river water quality assessment.

Keywords

• multi-source remote sensing
• random forest
• small and medium-sized rivers
• water quality parameters