Urban Water Quality Assessment Based on Remote Sensing Reflectance Optical Classification

Abstract

With the acceleration of urbanization, increasing water pollution means that monitoring and evaluating urban water quality are of great importance. Although highly accurate, traditional evaluation methods are time consuming, laborious, and vastly insufficient in terms of the continuity of spatiotemporal coverage. In this study, a water quality assessment method based on remote sensing reflectance optical classification and the traditional grading principle is proposed. In this method, an optical water type (OWT) library was first constructed using the measured in situ remote sensing reflectance dataset based on fuzzy clustering technology. Then, comprehensive scoring rules were established by combining OWTs and 12 water quality parameters, and water quality was graded into different urban water quality levels (UWQLs) based on the scoring results. Using the proposed method, the relative water quality of urban waterbodies was qualitatively evaluated at the macro level based on images from the multispectral imager of Sentinel-2. In addition, there was a significant positive correlation between the UWQLs and the water quality index (WQI). These results indicate the potential of this method for quantitative assessment of urban water quality, providing a new way to evaluate water quality using remote sensing algorithms in the future.

The Integration of Remote Sensing and Geographic Information System (Gis) in Managing Urban Ecosystems

“…As physical development in the study areas expands, demand for water may exceed supply, affecting water utilization. Also, the associated negative impacts of urbanization in these cities expose the available water sources to conditions such as pollution and dry-out from human activities and Urban Heat Island effects (Cai et al, 2021).…”

Long-Term Dynamics of Chlorophyll-a Concentration and Its Response to Human and Natural Factors in Lake Taihu Based on MODIS Data

“…where 𝑟 𝑎𝑤 is the skylight reflectance between air and water, which is valued to 0.022 for calm weather [20,34]. To establish and validate the optical estimation model, the fieldmeasured reflectance was calculated using corresponding simulated satellite reflectance, using the spectral response function [37,38]. Monthly hydro-meteorological data, covering air temperature (℃), sunshine duration (h), wind speed (m/s), and rainfall (mm) for the period 2002-2021 and collected by the meteorological station (red star, Figure . 1) were freely downloaded from the National Meteorological Data Center archive (http://data.cma.cn/(accessed on 9 May 2022)).…”

“…where r aw is the skylight reflectance between air and water, which is valued to 0.022 for calm weather [20,34]. To establish and validate the optical estimation model, the field-measured reflectance was calculated using corresponding simulated satellite reflectance, using the spectral response function [37,38].…”