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Journal Article

Green algae monitoring via ground-based GNSS-R observations

Wei Ban; Nanshan Zheng; Kefei Zhang; Kegen Yu; Shuo Chen; Qi Lu
GPS Solutions · Vol. 27, Issue 1 · 2023

Abstract

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.

Bibliographic Information

JournalGPS Solutions
PublisherSpringer
Publication Date2023-01-01
Publication Year2023
Volume27
Issue1
Document TypeJournal Article
Print ISSN1080-5370
eISSN1521-1886
DOI10.1007/s10291-022-01373-6

Access Information

NARA Access Coverage1995-01-01~Current
Journal Homepagehttps://www.springer.com/journal/10291
Publisher PageOpen Publisher Page
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