Green algae monitoring via ground-based GNSS-R observations
Green algae monitoring via ground-based GNSS-R observations
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DOI：
10.1007/s10291-022-01373-6
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发表时间：
2022-12
期刊：
影响因子：
4.9
通讯作者：
Wei Ban;Nanshan Zheng;Kefei Zhang;Kegen Yu;Shuo Chen;Qi Lu
中科院分区：
文献类型：
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作者：
Wei Ban;Nanshan Zheng;Kefei Zhang;Kegen Yu;Shuo Chen;Qi Lu
有害藻华（HABs）爆发具有频率高、范围大、危害加重的特点，而现有的人工和光学近红外遥感监测方法难以适应这些特点。我们提出了一种新的方法，监测绿色藻类使用全球导航卫星系统反射仪（GNSS-R）的观测。其基本原理是，由于绿色藻类的出现，海水介电常数和海面粗糙度发生变化，导致亮温升高，这可以根据反射时间延迟波形进行反演。利用青岛海域浒苔暴发期间采集的船载反射波形数据，建立了模式，并验证了模式的检测和估计性能。结果表明，GNSS-R反演绿色藻类密度的均方根误差为6.74%，表明GNSS-R技术在绿色藻类快速初步监测方面具有潜力。此外，GNSS-R技术具有成本低、返回时间短和不受气候限制等优点，因此可作为监测绿色藻类的一种新的有效手段。
Outbreaks of harmful algal blooms (HABs) exhibit high frequency, large range and damage aggravation characteristics, but existing monitoring methods, such as artificial and optical near-infrared remote sensing, cannot accommodate these characteristics. We propose a new method for monitoring green algae using Global Navigation Satellite System Reflectometry (GNSS-R) observations. The basic principle states that changes in the seawater dielectric constant and sea surface roughness due to the emergence of green algae lead to an increase in brightness temperature, which can be inverted based on the reflection time delay waveform. Shipboard reflection waveform data collected during an Enteromorpha prolifera outbreak in the Qingdao sea area were used for model development and validation of the detection and estimation performance. The results indicated that the root mean square error of GNSS-R-based inversion of the green algae density was 6.74%, indicating the potential of GNSS-R technology for rapid preliminary monitoring of green algae. Moreover, the advantages of a low cost, short return time and no climatic limitations support GNSS-R technology as a new and efficient means of green algae monitoring.