Green algae monitoring via ground-based GNSS-R observations

Abstract Outbreaks of harmful algal blooms (HABs) exhibit high frequency, large range and damage aggravation characteristics, but existing monitoring methods, such as artificial optical near-infrared remote sensing, cannot accommodate these characteristics. We propose a new method for green algae using Global Navigation Satellite System Reflectometry (GNSS-R) observations. The basic principle states that changes in the seawater dielectric constant sea surface roughness due to emergence lead an increase brightness temperature, which can be inverted based on reflection time delay waveform. Shipboard waveform data collected during Enteromorpha prolifera outbreak Qingdao area were used model development validation detection estimation performance. results indicated root mean square error GNSS-R-based inversion density was 6.74%, indicating potential GNSS-R technology rapid preliminary algae. Moreover, advantages low cost, short return no climatic limitations support efficient means monitoring.