## 5 Discussion
### Drift Prediction Analysis  
[PERSON] et al. [19; 20; 22] established the green tide drift prediction model based on the Lagrange particle tracking method. They integrated the model into an operational system called the remote sensing monitoring, forecasting, and early warning system of green tide disaster emergencies in the Yellow Sea. To specify the impact of simplification on prediction time, we calculated the 72 h drift prediction time of the four-phase distribution envelope before and after simplification. Figure 13 shows the predicted results of the distribution envelope on 6 June 2011. It can be found that the green tide generally moved to the northeast, which was consistent with the prevailing south wind in the Yellow Sea in summer as described in Section 2.1[21; 32], and that the green tide trajectory at point P was spiraling forward, which coincided with the regular semidiurnal tide at this point [47]. Table 3 shows that the time needed for simplified distribution envelope drift prediction was much shorter than without simplification. For example, for the green tide outbreak period on 9 July, the forecast time was shortened by 27 min, which can save time in making green tide disaster interception and removal plans. In the case of HY-1C with the transit time at 10:50, it takes about 3.5 h for data down-transmission, preprocessing, transmission to the user, green tide information extraction, and the drift prediction product production. At 13:50, the monitoring and forecast products could be transmitted to the field command department for emergency decision analysis. If the simplified method proposed in this paper were used, the product submission time would be about 13:20, half an hour earlier. Accordingly, the on-site interception and clearance would also be advanced by half an hour. Figure 12: Errors produced by the SM-ADSL, Douglas–Peucker and equidistance methods: (**A**) algebraic errors, and (**B**) overall errors. The following should be noted: (1) Drift prediction experiments were conducted on the simplified envelopes of the other two methods, and it was found that the time required for prediction was consistent with the SM-ADSL. This indicates that the time needed for prediction depends on the number of remaining vertices after simplification. This result corresponds to the method used for green tide drift prediction, the Lagrangian particle method [19; 48; 49]. The prediction time for each particle is the same, so the number of particles determines the prediction calculation time. To avoid repetition, the time required for drift prediction by the other two methods was not listed. (2) The accuracy of the green tide predicted boundary mainly relies on the precision of wind, current, and drift prediction models. At the same time, a simplified boundary with minimal deformation also contributes to the accuracy of the predicted boundary. The evaluation of the predicted boundary's accuracy requires consideration of both the prediction model and continuous monitoring, which is beyond the main scope of this study and should be further explored in subsequent research. The Impact of Monitoring Results from Different Satellite Sources on Decision-Making Plan Formulation  
The detection capabilities of satellite images with different spatial resolutions vary [5; 50; 51; 52]. This is because the monitored green tide information and distribution envelope will inevitably differ, and these differing monitoring results will necessarily impact the formulation of decision-making plans. To investigate the influence of different  
Figure 13: (**A**) Predicted drift of the green tide initial envelope on 6 June 2021 after 24, 48, and 72 h, and (**B**) green tide predicted trajectory of the 72 h drift at point P (shown as brown in both (**A**,**B**)), which is a vertex on the envelope.