# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 1 Introduction
The remote sensing of algal bloom using GNSS-R has become an interesting topic. [PERSON] et al. [23] proposed using bistatic radar to monitor algal bloom in the Gulf of Mexico for the first time. [PERSON] et al. [24] studied the change of the sea surface roughness and dielectric constant after being covered by the red tide. [PERSON] et al. [25] firstly analyzed the feasibility of spaceborne GNSS-R algal bloom detection in Taihu Lake, China. They used a GNSS-R observable called the power ratio (PR), with Sentinel-3 OLCI data collected from April to August 2020 as a reference, and discussed the influence of wind speed on the detection accuracy. They regarded PR > 2 as coherent reflection or the existence of bloom, and they found that in the wind speed section of 1-2.5 m/s, the PR-based detection method can achieve the best detection accuracy. Subject to the temporal resolution of Sentinel-3 and the rainy summer climate of Taihu Lake, actually, only 9 days of Sentinel reference data and only 120 preprocessed GNSS-R data points could be obtained. In summary, the results and analyses of the current research are intuitive, and the amount of employed data is insufficient. Additionally, wind speeds of less than 1 m/s are not discussed, making the research results not convincing nor robust enough without comprehensive assessment of the detection results. Although [PERSON] et al. [25] considered the influencing factor of wind speed, other related meteorological factors, such as wind direction and precipitation, remain to be investigated. Some meteorological factors, such as temperature and solar radiation, do not immediately cause algal bloom. They mainly take effect in the growth stage of plankton, which are also worth studying. To further verify the feasibility of detecting algal bloom with GNSS-R and meteorological data, more observed GNSS-R data and more meteorological factors with time memory should be involved. The machine learning (ML) method is an effective tool in remote sensing [26; 27]. Specifically, it has successfully addressed such problems as classification and regression in remote sensing precisely and effectively. Since the adoption of neural networks for sea ice sensing [28], combining ML with GNSS-R has become a new tendency in recent years. For instance, [PERSON] et al. [29] applied decision tree (DT) and random forest (RF) in GNSS-R sea ice concentration (SIC) inversion. Ref. [30] employed ML methods in GNSS-R SM retrieval. In practical research, classification problems may occur such that the data of different labels are imbalanced ('imbalance' refers to the disproportionate proportion in the class label). Specifically, the data of one category can be much sparser than those of other types, but the importance of the minority should be emphasized despite its lower proportion. Algal bloom detection via GNSS-R faces such a challenge of an imbalanced dataset. In other words, algal bloom will not take up a great proportion in the whole dataset, but its damage is non-negligible. Traditional classifiers assume that the number of points in each class is generally comparable, and they aim to reach the highest overall accuracy (OA). However, if applying these traditional classifiers to imbalanced datasets, the minority class will be neglected due to its small contribution to the OA, leading to the failure of identifying the minority class. In order to deal with it, the random under sampling (RUS) boosted (RUSBoost) algorithm [31], an algorithm designed for processing imbalanced datasets, can be deployed. RUSBoost is an algorithm that combines RUS with adaptive boosting (AdaBoost, an algorithm that trains many weak classifiers and integrates them into a strong classifier to improve the classification performance; more details are in [32]) and takes advantage of both of them. The RUS algorithm randomly removes samples from the majority class until its number of samples is equal to that of the minority class.