Investigation of water quality in the shallow coastal waters of the Persian Gulf

被引:1
|
作者
Barkhordar, Hossein [1 ]
Mohammadpour, Gholamreza [2 ,4 ]
Hassanzadeh, Smaeyl [1 ]
Karemi, Hajar [3 ,4 ]
机构
[1] Univ Isfahan, Fac Phys, Esfahan 8174673441, Iran
[2] Univ Laval, Iranian Natl Inst Oceanography & Atmospher Sci, Quebec City, PQ, Canada
[3] Oceanog Khorramshahr Univ Marine Sci & Technol, Fac Marine Sci, Dept Phys Oceanog, Khorramshahr, Iran
[4] Laval Univ, Quebec City, PQ, Canada
关键词
Chlorophyll; Machine learning; Bushehr Bay; Persian Gulf; CHLOROPHYLL-A; REMOTE ESTIMATION; REFLECTANCE; ALGORITHMS; VEGETATION; LEAF; SENTINEL-2; INDEXES; SURFACE; CANOPY;
D O I
10.1016/j.marpolbul.2024.117263
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Advanced satellite technology and algorithms are making substantial progress in meeting the need for improved environmental monitoring of coastal waterways. Integrating high-resolution satellites with in-situ radiometric equipment is essential for effectively monitoring algal blooms and managing coastal resources. Our work has built a model to examine geographical and temporal fluctuations in chlorophyll-a concentration in Bushehr Bay, Persian Gulf, Iran, using radiometric data and high-resolution remote sensing. In this study, we used twenty-four bio-optical features for analysis. After evaluating and selecting the most important features, we used the top five features to estimate chlorophyll-a concentration using machine learning algorithms. Likewise, the model could effectively investigate our climatology of chlorophyll in the study area. Our findings provide a dependable approach to monitor the environmental effect of chlorophyll-a and enhance water quality and regional management of primary production in coastal waters. This proposed proxy may be implemented in comparable places globally.
引用
收藏
页数:13
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