Remote sensing-based water quality assessment in Nansi Lake of Northern China

Zihui Liu; Baolei Zhang

doi:10.1117/12.3035493

10 July 2024 Remote sensing-based water quality assessment in Nansi Lake of Northern China

Zihui Liu, Baolei Zhang

Author Affiliations +

Proceedings Volume 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024); 132232L (2024) https://doi.org/10.1117/12.3035493
Event: 2024 5th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2024), 2024, Wuhan, China

Abstract

Remote sensing has long been an effective method for water quality monitoring. However, traditional empirical and analytical methods may suffer from poor accuracy for non-optically active parameters. Taking Nansi Lake as the study area, the research conducts chlorophyll-a and total nitrogen concentrations assessment using the K-Nearest Neighbor (KNN) and Backpropagation (BP) neural network methods based on the measured water quality data in 2020 and Landsat remote sensing images. The results indicate that (1) Spectral features exhibiting stronger correlations with chlorophyll-a concentration include Blue/Green, (Blue-Green)/Green, and (Blue-Green)/Red, while those exhibiting stronger associated with total nitrogen concentration encompass Brightness, Green-Red, and (Green-Red)/Blue. (2) Through rigorous model evaluation, the K-nearest neighbor method emerged as the most effective approach. The determination coefficients for chlorophyll-a and total nitrogen model test sets were 0.60 and 0.56, respectively, with corresponding root mean square errors of 1.34 μg/L and 0.86 mg/L. (3) On February 18, 2020, relatively higher chlorophyll-a concentrations were observed in Nanyang Lake and Zhaoyang Lake, while higher total nitrogen levels were recorded in Dushan Lake.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zihui Liu and Baolei Zhang "Remote sensing-based water quality assessment in Nansi Lake of Northern China", Proc. SPIE 13223, Fifth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024), 132232L (10 July 2024); https://doi.org/10.1117/12.3035493

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KEYWORDS

Water quality

Data modeling

Education and training

Statistical modeling

Landsat

Remote sensing

Environmental monitoring

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