# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 4 Experiments and Evaluation
### Error Analysis  
To better find the error sources in the detection results, we mapped the correctly classified and misclassified points on a day together. Through the observation, we discovered that the misclassified points mostly appear near the edge of the actual cyanobacteria bloom-covered zone. On 30 May 2021, cyanobacteria accumulated and covered almost the entire lake (Figure 7a). On this day, 11 CYGNSS points were available in the test dataset, and they all correctly classified as positive for the existence of cyanobacteria bloom, showing the great accuracy of the proposed method. On the days that nearly no cyanobacteria bloom occurred, such as 17 August 2020 (Figure 7b), the detection results are also satisfactory, and mistakes are seldom seen in this situation. Therefore, we conclude that the detection accuracy of this method is reliable when the surface condition is rather uniform (no cyanobacteria bloom or massive coverage). As Figure 7c shows, our method also shows the ability to identify the cyanobacteria bloom zone to a certain degree when a small amount of cyanobacteria bloom exists. Misclassification usually occurs when the lake is partially covered by cyanobacteria bloom. We take 22 May 2019 and 11 October 2020 (Figure 7d,e) as examples where half of the total points are classified incorrectly (3/7 and 4/8, respectively). By inspecting the misclassified point locations with the cyanobacteria bloom area on that day, the misclassified points usually are located not far away from the cyanobacteria bloom zone, which covers a considerable area of the lake. We speculate that the probable reason for it is that the cyanobacteria bloom zone nearby not only limits the wave height in the zone itself but also the surrounding area, even the whole lake. Although this situation is a kind of misclassification, it does not influence the application value of this method seriously, as it can detect the possible cyanobacteria bloom existence in the lake, especially for some real-world applications, where exact edge information is not required. Relative to optical remote sensing data, it has progressed a lot since this method can warn of the dangers of cyanobacteria bloom occurrence during overcast weather, but optical data are not accessible. Another kind of misclassification is from the confusing weather conditions. On 23 May 2019 (Figure 7f), most points are judged incorrectly (11/13). On this day, only some parts of the lake surface are covered by cyanobacteria bloom, but the average wind speed near the SPs is mostly less than 3 m/s, leading to coherent reflection from the lake surface, while the average wind speed ten days before the day is around 3.5 m/s. The average temperature is more than 21 \({}^{\circ}\)C, and the average atmosphere pressure is around 1010 hpa on the observing day and ten days before, which are generally suitable for the growth and gathering of cyanobacteria, but not much cyanobacteria bloom actually emerges. This phenomenon is not usual in the research period. It needs to be pointed out that despite the involvement of meteorological factors successfully reducing the misclassification to a large extent, they still cannot overcome it completely. More countermeasures remain to be proposed in the future to further decrease the misclassification rate of GNSS-R algal bloom detection.  
Figure 7: _Cont._