Harmful Algae Blooms Detection Using GNSS-R MSS Observations

As a serious marine environmental disaster, harmful algal blooms (HABs) exhibit characteristics such as high frequency, large impact area, and increasing damage. There is an urgent need for an all-weather, high revisit rate, wide-range, and large-scale monitoring method to address these changes. In this article, we propose a method to detect the distribution and density of HABs using the mean square slope (MSS) observations from Global Navigation Satellite System-Reflectometry (GNSS-R), based on the physical explanation that the coverage of HABs leads to a reduction in wind-driven sea surface roughness. By comparing the Cyclone Global Navigation Satellite System (CYGNSS) MSS data with actual observations, the applicable threshold for HABs monitoring based on MSS is clarified, and a HABs density inversion model is constructed. The classification of two severe HABs areas (Jiaozhou Bay and the Gulf of Mexico) has been achieved with a 59% probability of detection and 1% false alarm rate. The introduction of this method enables daily scale observations of HABs, which can better reveal the spatiotemporal distribution characteristics, migration patterns, and influencing factors of HABs, providing a new means for the monitoring and integrated management of HABs.