# Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
## 4 Discussion  
### Supporting the Optimum Satellite Sensor Selection for Flood Observation  
The experiment constructed three flood water observation tasks corresponding to the three evolutionary flood phases, and 16, 15, and 14 satellite sensors were discovered, evaluated, and ranked by utilizing the proposed OCEM to quantify their expected observation performances. The OCEM was effective in accurately quantifying the satellite sensor observation capability with spatiotemporal characteristics of the flood event considered, enabling the optimal selection of the flood-observation-oriented sensor, thereby supporting the real-time acquisition of flood observation information. Taking the OCEM results of the preparedness-phase task (Figure 4) as an example, the results show that the three microwave sensors ranked the highest. As shown in Table 5, their high ranking is mainly because they are unaffected by clouds, whereas other optical sensors are greatly affected. Specifically, their EnIm values are 1, indicating that they have larger valid observations than optical sensors. Therefore, the proposed OCEM is more effective and accurate in practical application because it considers the impact of a real-time environmental influential factor (cloud cover). For example, six optical sensors with an OCEM value of 0 in the response-phase task (Figure 5) were found because their EnIm values are 0 (Table 6), indicating that optical sensors are completely unusable. Benefiting from the flexible weight adjustment mechanism, the weights of the linearly correlated factors can be set dynamically on the basis of the spatiotemporal characteristics of a specific flood phase. Taking the recovery-phase task as an example, it prefers the sensor that can obtain high-quality observation data. As shown in Table 10, the weights of observation quality factors, SpaRes, SpeRes, and RadRes, account for 86.4% of the weight in total, and the three other factors only take up 13.6%, which complies with the high-quality observation requirement. Figure 6 shows a distinctive drop between NAOMI and CZI. As shown in Table 7, the optical sensors on the left of the drop have SpaRes values greater than 0.1, and sensors on the right of the drop have SpaRes values less than 0.1, proving the effectiveness of the weight adjustment mechanism indirectly. Notably, there are various methods for assigning weights apart from the AHP. These methods generally fall into three categories: subjective, objective, and hybrid [52]. Subjective methods, such as the \"Full Consistency Method\" [53] and \"Level Based Weight Assessment\" [54], rely on the decision maker's experiences and preferences. They are flexible but may be prone to biases and variations among decision makers. Objective methods like \"Entropy\" [55] and \"Criterion Impact Loss\" [56] calculate weights using quantitative data, statistical analysis, and mathematical models. These methods are more systematic and less biased. Hybrid methods like \"Integrated Determination of Objective Criteria Weights\" [56] and \"Entropy-Critic-PROMETHEE\" [57] combine aspects of both subjective and objective methods, offering a more comprehensive view and aiming to counter weaknesses found in individual approaches. Specifically, AHP was selected for several reasons. Firstly, the number of sensors (samples) queried each time is limited, which prevents the use of objective and hybrid methods for weight calculation. Moreover, different observation capability factors should be assigned different weights in different phases, while an objective approach may assign the same weight across phases. Although the AHP's effectiveness has been demonstrated through experiments, the potential of other subjective methods in the OCEM should not be dismissed and warrants further exploration. The flood response department can select the optimum sensor on the basis of the ranking of OCEM values.