Water Turbidity Monitoring of Zhiyin and Huangjia Lakes in Wuhan for COVID-19 Epidemic Using HY-1C CZI Data

ZHOU Qu; LIU Jianqiang; WANG Jianru; DENG Shiquan; TIAN Liqiao

doi:10.13203/j.whugis20200101

Volume 45 Issue 5

May 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(5): 676-681. > DOI: 10.13203/j.whugis20200101

ZHOU Qu, LIU Jianqiang, WANG Jianru, DENG Shiquan, TIAN Liqiao. Water Turbidity Monitoring of Zhiyin and Huangjia Lakes in Wuhan for COVID-19 Epidemic Using HY-1C CZI Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 676-681. DOI: 10.13203/j.whugis20200101

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Water Turbidity Monitoring of Zhiyin and Huangjia Lakes in Wuhan for COVID-19 Epidemic Using HY-1C CZI Data

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China
3.
MCFLY Technology Company Limited, Beijing 100102, China

Funds:

The National Key Research and Development Program of China 2018YFB0504900

The National Key Research and Development Program of China 2018YFB0504904

The National Key Research and Development Program of China 2016YFC0200900

the National Natural Science Foundation of China 41571344

the National Natural Science Foundation of China 41701379

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Author Bio:
ZHOU Qu, postgraduate, specializes in ocean color remote sensing and satellite cross-calibration. E-mail:quzhou@whu.edu.cn
Corresponding author:
TIAN Liqiao, PhD, professor. E-mail: tianliqiao@whu.edu.cn
Received Date: March 20, 2020
Available Online: July 26, 2023
Published Date: May 04, 2020

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Abstract

Abstract

Water turbidity is one of the most important parameters of inland lake water quality and an important influencing factor of water environment ecosystem.Affected by coronavirus disease 2019(COVID-19) epidemic, during the period from January 25, 2020 to February 5, 2020, Wuhan government built two emergency hospitals, namely Huoshenshan Hospital and Leishenshan Hospital, to cope with the epidemic. In the process of hospital construction, dynamic monitoring of turbidity in the adjacent waters is helpful for epidemic prevention and government decision-making. Using Chinese high-resolution Haiyang-1C (HY-1C) coastal zone imager (CZI) data, combined with high-frequency in-situ turbidity measurements, a turbidity model uitable for Wuhan waters is established (determinant coefficient: R²>0.85; root mean square error: RMSE < 5.50 NTU). Through the analysis of the variations of turbidity at Zhiyin, Huangjia, and Zhushan lakes before, during, and after constructions of Huoshenshan or Leishenshan hospitals, we find out that the variation trends of waters near and away from Huoshenshan or Leishenshan hospitals are similar, which indicates that the constructions of the two hospitals doesn't affect the nearby waters. This study also shows that Chinese HY-1C CZI has the potential to retrieve turbidity in small inland waters.
- Haiyang-1C satellite,
- coronavirus disease 2019(COVID-19),
- turbidity retrieval,
- rainfall

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[1]	Nechad B, Ruddick K G, Park Y. Calibration and Validation of a Generic Multisensor Algorithm for Mapping of Total Suspended Matter in Turbid Waters[J]. Remote Sensing of Environment, 2010, 114(4):854-866 doi: 10.1016/j.rse.2009.11.022
[2]	Jayson-Quashigah P N, Appeaning A K, Amisigo B, et al. Assessment of Short-Term Beach Sediment Change in the Volta Delta Coast in Ghana Using Data from Unmanned Aerial Vehicles (Drone)[J]. Ocean and Coastal Management, 2019, 182: 104952-104966 doi: 10.1016/j.ocecoaman.2019.104952
[3]	Dogliotti A I, Ruddick K G, Nechad B, et al. A Single Algorithm to Retrieve Turbidity from Remotely-Sensed Data in All Coastal and Estuarine Waters[J]. Remote Sensing of Environment, 2015, 156:157-168 doi: 10.1016/j.rse.2014.09.020
[4]	Zhou Q, Tian L, Wai O W H, et al. Impacts of Insufficient Observations on the Monitoring of Short- and Long-Term Suspended Solids Variations in Highly Dynamic Waters, and Implications for an Optimal Observation Strategy[J]. Remote Sensing, 2018, 10(2):345-354 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=MDPI000000195354
[5]	Cao Z, Duan H, Feng L, et al. Climate- and Human-Induced Changes in Suspended Particulate Matter over Lake Hongze on Short and Long Timescales[J]. Remote Sensing of Environment, 2017, 192:98-113 doi: 10.1016/j.rse.2017.02.007
[6]	Chen Z, Hu C, Muller-Karger F. Monitoring Turbidity in Tampa Bay Using MODIS/Aqua 250-m Imagery[J]. Remote Sensing of Environment, 2007, 109(2):207-220 doi: 10.1016/j.rse.2006.12.019
[7]	Zhou Q, Li J, Tian L, et al. Coupled Approach for Radiometric Calibration and Parameter Retrieval to Improve Water Quality Estimations in Turbid Inland/Coastal Waters[J]. Optics Express, 2020, 28(4):5567-5586 doi: 10.1364/OE.384035
[8]	Luo Q, Zhang X, Li Z, et al. The Effects of China's Ecological Control Line Policy on Ecosystem Services:The Case of Wuhan City[J]. Ecological Indicators, 2018, 93:292-301 doi: 10.1016/j.ecolind.2018.05.009
[9]	Huang X, Wang Y. Investigating the Effects of 3D Urban Morphology on the Surface Urban Heat Island Effect in Urban Functional Zones by Using High-Resolution Remote Sensing Data:A Case Study of Wuhan, Central China[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 152:119-131 doi: 10.1016/j.isprsjprs.2019.04.010
[10]	Du N, Ottens H, Sliuzas R. Spatial Impact of Urban Expansion on Surface Water Bodies-A Case Study of Wuhan, China[J]. Landscape and Urban Planning, 2010, 94(3):175-185 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dd1bcf9e4c6f03d369b8a5935176c8a1
[11]	Matsuzaki K, Murase O, Sugishita K, et al. Optical Characterization of Liposomes by Right Angle Light Scattering and Turbidity Measurement[J].Biochimica et Biophysica Acta Biomembranes, 2000, 1467(1):219-226 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=4d824141e7856702dd3de610b11373b7
[12]	Wang M, Shi W. The NIR-SWIR Combined Atmospheric Correction Approach for MODIS Ocean Color Data Processing[J]. Optics Express, 2007, 15(24):15722-15733 doi: 10.1364/OE.15.015722
[13]	Tong C, Mu B, Liu R J, et al. Atmospheric Correction Algorithm for HY-1C CZI over Turbid Waters[J]. Journal of Coastal Research, 2019, 90(sp1):156-164 doi: 10.2112/SI90-019.1
[14]	Robert E, Grippa M, Kergoat L, et al. Monitoring Water Turbidity and Surface Suspended Sediment Concentration of the Bagre Reservoir (Burkina Faso) Using MODIS and Field Reflectance Data[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 52:243-251 doi: 10.1016/j.jag.2016.06.016
[15]	Espinoza Villar R, Martinez J M, Le Texier M, et al. A Study of Sediment Transport in the Madeira River, Brazil, Using MODIS Remote-Sensing Images[J]. Journal of South American Earth Sciences, 2013, 44:45-54 doi: 10.1016/j.jsames.2012.11.006
[16]	Wong T T. Performance Evaluation of Classification Algorithms by k-Fold and Leave-One-Out Cross Validation[J]. Pattern Recognition, 2015, 48(9):2839-2846 doi: 10.1016/j.patcog.2015.03.009

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Water Turbidity Monitoring of Zhiyin and Huangjia Lakes in Wuhan for COVID-19 Epidemic Using HY-1C CZI Data

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