# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 5 Conclusions  
This research presents a machine learning algorithm-based GNSS-R algal bloom detection method with auxiliary meteorological data. GNSS-R observables extracted from CYGNSS DDMs and meteorological factors are selected as input to the RUSBoost model, an algorithm designed for classifying imbalanced datasets. In the beginning, we proved the feasibility of detecting algal bloom by a single GNSS-R observable using the manual threshold value method and achieved a detection accuracy around 65%. To pursue further improvement in the detection results, we introduced meteorological reanalysis data of ERA5-Land and the ML method. By comparing different feature combinations, we can conclude that with the aid of meteorological factors, the detection accuracy can be improved to a relatively great extent, especially when all the meteorological factors we considered are involved (test TPR = 82.9%, TNR = 81.9%, OA = 82.0% and AUC = 0.88). The influence of different meteorological factors on the final result was also evaluated. Among all other factors, temperature and solar radiation downwards have the best correlation with the bloom conditions, with improvements of around 10% being observed in the results, though lower than the feature combination containing all meteorological factors. Finally, we discussed the performance of the proposed method under different surface conditions and the probable reason for the mistakes in the detection results, and found that when the lake surface is rather uniform (nearly full of algal bloom or with no bloom), the performance is plausible, while on days where the lake surface is partly covered by algal bloom, the mistake rate increases. Moreover, there are still some conditions in which GNSS-R and meteorological data are not enough to successfully distinguish the existence of algal bloom. Some problems remain to be solved in the future. The detection accuracy needs to be further progressed, which may be realized by applying other ML methods, and more features can be considered. Besides this, the employment of meteorological reanalysis data in this research represents the mechanism of algal bloom growth and dissolves the uncertainty by GNSS-R coherent detection. For comparison, the measured in situ meteorological data can be utilized, and the performance between the measured data and that of  
Figure 7: Detection results on different days during the research period, where green area is cyanobacteria bloom coverage (NDVI > 0.05), and red points are the SPs being classified correctly, while the red crosses are misclassified. ERA5-Land reanalysis data should be discussed. After the classification method becomes mature, regression analysis will be conducted. Conceptualization, methodology, validation and writing--Y.Z. and Q.Y. ; supervision, Q.Y. ; funding acquisition, Q.Y. All authors have read and agreed to the published version of the manuscript. The research was supported in part by the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China, under Grant KLSMNRG202206; and in part by the National Natural Science Foundation of China under Grant 42001362. Data Availability Statement: Data can be accessed upon request from the links mentioned in Section 2. The authors are grateful to NASA EOSDIS Physical Oceanography Distributed Active Archive Center (DAAC), Jet Propulsion Laboratory, Pasadena, CA, USA, for making the CYGNSS data available, at [[https://www.esrl.noaa.gov/psd/](https://www.esrl.noaa.gov/psd/)]([https://www.esrl.noaa.gov/psd/](https://www.esrl.noaa.gov/psd/)), accessed on 1 June 2022. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: