Water Quality Monitoring Research Analysis Based on the Remote Sensing Technology
DOI:
https://doi.org/10.54097/xqa3y621Keywords:
RS, Monitoring, Water quality.Abstract
At present, supply and demand contradictions in water resources around the world are imminent and monitoring and governance of the water environment is urgent. This paper aims to combine space and aerospace remote sensing technology and water quality monitoring fields, fully consider the impact of current water body optical polarization on the accuracy of remote sensor optical signals, to identify the lower cost, more accurate measurement data, and easier to operate detection methods. The main analysis and study of three common water quality variables in water quality testing methods and the combination and synthesis of aerospace remote sensing data are proposed. In addition, this paper also selects the relevant water quality testing cases and the impact of optical polarization on the same type of parameter monitoring and performs a parameter analysis, estimating the financial cost that can be saved annually using remote sensing technology for water quality monitoring. Finally, the importance of the combination of space remote sensing data and aerial remote sensing data to improve the accuracy of water quality monitoring data is summarized, and the development prospect of the integration of air remote sensing and space remote sensing technology to overcome the impact of water multi-angle polarization on the accuracy of remote sensing data has prospected.
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References
Liu, S. Q., (2018) According to the United Nations report, water shortages will affect five billion people worldwide by 2050. China Meteorological News, 2018 - 04 - 02 (001).
Zhu, X., Liu, L. M., Ye, Z. L., (2021) Methods for remote drone water quality monitoring [J]. China Shipping, (07) pp157 - 159.
Huang, X. X, Ying, H. T., Xia, K., (2020) Replay of water quality parameters based on drone multi-spectral images and the OPT-MPP algorithm [J]. Environmental science, (08) pp 3591 – 3600.
Professional Copy: Remote Sensing of Water Environment and Its Applications – Written at the twenty-eighth World Water Day. (2020.3.23) University of China Institute of Mapping Science and Technology. https://sges.sysu.edu.cn/article/486.
Wang, S. M., Qing, B. Q., (2023) Progress in the study of remote sensing monitoring of lake water quality parameters. 44 (3) pp 1228 - 1243.
Wang, B., Huang, J. H., Guo, H. W., et al. (2022) Progress in research on inland water quality monitoring based on remote sensing[J]. Water Resources Protection, 38 (3) pp 117 - 124.
Research progress-Research on a model based on Landsat series of satellites to estimate the transparency of lakes in China. (2021.1.28) Nanjing Institute of Geography and Lakes Research of the Chinese Academy of Sciences. http://niglas.ac.cn/xwdt_1_1/yjjz/ 202101/t20210128_5878485.html.
Yang, X. Q., Tong, R. Q., Ma, L., Li, J., Wang, S. Q., Tian, L. Q., (2023) Monitoring water color anomaly of lakes based on an integrated method using Landsat-8 OLI images, International Journal of Digital Earth, 15: 1 pp 1567 - 1587.
Zeng, W.; Xu, K.; Cheng, S.; Zhao, L.; Yang, K., (2023) Regional Remote Sensing of Lake Water Transparency Based on Google Earth Engine: Performance of Empirical Algorithm and Machine Learning. Appl. Sci. 2023, 13, 4007.
Wu, T. X., Yan, L., Xiang, Y., et al. (2010) multi-angle polarization spectral characteristics of water bodies and their applications in water color remote sensing [J]. Spectrology and Spectral Analysis, 30 (2) p 448.
Ren, W. X., Wu, X. D., Nie, H. F., Xiao, C. L., Ge, X. G., Yang, J. Y., Luo, L., (2022) Optical attenuation characteristics of Lake Daye, Hubei Province based on remote sensing inversion of Landsat 8 OLI. Journal of Lake Sciences, 34 (3) pp 791 - 803.
Miao, Q.; Liu, C.; Tan, X.H.; Liu, Z.Q.; Gao, Y., (2009) Evaluation of the eutrophication of South Four Lake in China through Remote Sensing Technology. Proceedings of the first International Conference on Future Computers and Communications (FCC 2009).
Cai, X.L.; Li, Y.M.; Lei, S.H.; Zeng, S.; Zhao, Z.L.; Lyu, H.; Dong, X.Z.; Li, J.D.; Wang, H.J.; Xu, J.; et al., (2023) Case study of inland lake waters in eastern China. Sci., 856, 158869.
Amani, M.; Ghorbanian, A.; Ahmadi, S. A.; Kakooei, M.; Moghimi, A.; Mirmazloumi, S. M.; Moghaddam, S. H. A.; Mahdavi, S.; Ghahremanloo, M.; Parsian, S.; et al. (2020) Google Earth Engine Cloud computing platform for remote sensing big data applications: a comprehensive review. IEEEJ. Sel. Top. Appl, 13 pp 5326 - 5350.
Paul, A.; Vignesh, K.S.; Sood, A.; Bhaumik, S.; Singh, K.A.; Sethupathi, S.; Chanda, A., (2023) Suspended Particulate Matter Analysis ofPre and during Covid Lockdown Using Google Earth Engine Cloud Computing: Case study of Wukai Reservoir. Bull. environment. Contam. Toxicol, 110, 7.
Page, B.P.; Olmanson, L.G.; Mishra, D. R., (2019) A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems. Remote Sens. Environ, 231, 111284.
Callejas, I.A.; Huang, L.; Cira, M.; Croze, B.; Lee, C.M.; Cason, T.; Schiffler, E.; Soos, C.; Stainier, P.; Wang, Z.; et al., (2023) Use of Google Earth Engine for Teaching Coding and Monitoring of Environmental Change: A Case Study among STEM and Non-STEM Students. Sustainability, 15, 11995.
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