Detecting Algal Bloom Using Cygnss and ERA-5 Data

Algal bloom has become a serious environmental problem caused by the overpropagation of planktons in many water-bodies, and effective remote sensing methods for monitoring it are urgently needed. Global Navigation Satellite System (GNSS)-Reectometry (GNSS-R) has been developed rapidly these years. The reflected GNSS signals received by GNSS-R satellites carry information (e.g., the surface roughness) about the specular points. When algal bloom emerges, the water surface will turn smoother, which could be detected by GNSS-R. In addition, meteorological factors also perform a key role in the formation of algal bloom. In this article, a new machine learning aided GNSS-R algal bloom detection method with the auxiliary of meteorological data is established. This work employs the Cyclone GNSS (CYGNSS) data and the fifth generation of European Reanalysis data with the application of Random Under-Sampling Boost (RUS-Boost) algorithm. During the evaluation stage, the test True Positive Rate of 80%, overall accuracy of 84.1% and the Area Under (Receiver Operating Characteristic) Curve of 0.9 were achieved when all the considered GNSS-R observables and meteorological factors are involved. Meanwhile, the contribution of each meteorological factor was also evaluated.