# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 4 Experiments and Evaluation
### Machine Learning Method
#### 4.2.2 Detection Results  
Another performance index suitable for evaluating the imbalanced data classification effect is area under curve (AUC), also employed here for evaluating the performance of the RUSBoost algorithm. In addition, AUC is an indicator calculated from the receiver operating characteristic curve (ROC; it is in the coordinate system where the TPR is on the y-axis and the false positive rate (FPR) is on the x-axis [45]), which is the size of the area under ROC and also shows the performance of the classifier. The detection results of different feature combinations are listed in Table 2, and the visualization of Table 2 is shown in Figure 6. We considered eight different feature combinations, and they can be roughly divided into three groups: only GNSS-R observables (combination A in Table 2); GNSS-R observables + all employed meteorological factors (combination H); and GNSS-R observables + one of the employed meteorological factors (combinations B-G). In these three groups, one can come to the conclusion that the involvement of meteorological factors can apparently improve the detection accuracy, while the contributions of each meteorological factor are varied. When no meteorological factors are involved, the accuracy results for cyanobacteria bloom detection (TPR), clean lake surface detection (TNR), OA and AUC are 63.4%, 68.9%, 68.7% and 0.70, respectively. Compared with the threshold value method used before, there is almost no significant improvement in the combination of the three observables, but the application potential of GNSS-R in this field can still be proven because the AUC value here is greater than 0.5, which indicates the effectiveness of the imbalanced data classification. After all the meteorological factors were engaged, the performance indexes improved a lot and were able to reach 81.9%, 82.9%, 82.0% and 0.88 in the four performance indexes, respectively, gaining 10-20% more than before, showing the prospect of the GNSS-R cyanobacteria bloom detection method by taking meteorological factors into consideration. Through doing so, some disturbing factors are excluded. For instance, on one certain day, the wind speed is quite low, so the coherent reflection from the lake surface is received such that it looks like cyanobacteria bloom exists, but the temperature on this day, or in the period ten days before, is lower than 10 \({}^{\circ}\)C (which hinders cyanobacteria growth), so the possibility of cyanobacteria bloom occurrence can be ruled out. This proposed method effectively overcomes the shortcomings of GNSS-R data (failure to identify the actual reason that leads to the smoothness of the lake surface). Then, the contributions by single meteorological factor to the final result were evaluated. In Table 2 and Figure 6, the performance of combination B-G is better than that of combination A but worse than that of combination H. This means that the six meteorological factors involved are effective in the research, and considering all of them can produce the best result. Temperature (combination C) and solar radiation (combination G) contribute most compared to other factors. The two combinations improve by 8.7%, 7.4% in OA, 14.6%, 14.6% in TPR, 8.5%, 7.1% in TNR and 0.17, 0.14 in AUC, respectively, relative to combination A. The result that temperature and solar contribute most to cyanobacteria bloom detection fits the conclusion in [43] well, in which it is found that suitable light and temperature are the key factors that affect the growth of cyanobacteria bloom.  
Figure 6: Line chart of the results in Table 2. The letters at the botton of the figure refer to the feature combinations in Table 2.