Sea Surface Green Algae Density Estimation Using Ship-Borne GEO-Satellite Reflection Observations

被引:5
|
作者
Ban, Wei [1 ]
Zheng, Nanshan [2 ]
Yu, Kegen [2 ]
Zhang, Kefei [2 ]
Liu, Jinxiang [2 ]
机构
[1] Wuhan Univ, Chinese Antarctic Ctr Surveying & Mapping, Wuhan 430079, Hubei, Peoples R China
[2] China Univ Min & Technol, Sch Environm Sci & Spatial Informat CESI, Xuzhou 221116, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Algae; Green products; Dielectric constant; Satellites; Surface roughness; Rough surfaces; Reflection; Enteromorpha prolifera monitoring; geostationary Earth orbit (GEO) satellite; global navigation satellite systems-reflectometry (GNSS-R); green algae; mixture dielectric constant; DISPERSION;
D O I
10.1109/LGRS.2022.3198253
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
In recent years, global navigation satellite systems-reflectometry (GNSS-R) technology has been increasingly considered for applications in sea surface monitoring. This article presents a new method to retrieve the density of sea surface green algae by using the reflected signals of geostationary Earth orbit (GEO) satellites collected by shipborne receiver. Because GEO satellites are stationary relative to a fixed receiver on the Earth's surface, the reflected GEO (GEO-R) satellite signals are not affected by Doppler frequency or elevation angle, which can greatly simplify the modeling of the reflected power and realize continuous green algae monitoring in the same area. Specifically, the influence of green algae on GEO-R power through varying reflection coefficient and roughness was analyzed. Then, an empirical model was established to retrieve the green algae density by using the GEO-R power. Finally, the experimental data collected in Qingdao, Jiaozhou bay, were used to verify the developed models, and the results show that the inversion accuracy of the green algae density model is better than 4%.
引用
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页数:5
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