# Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
## 4 Discussion
### Superiority of Ocem  
This section aims to affirm the superiority of the OCEM by conducting both quantitative and qualitative comparisons with other relevant methodologies. Initially, the DOCI model [29] is selected for quantitative comparison because it is the most relevant and advanced sensor observation capability pre-evaluation model. Following this, the OCEM was qualitatively compared with seven related approaches. The details are as follows. In Figure 7, the comparison between the DOCI and the OCEM across all three observation tasks is represented, focusing solely on optical sensors due to the DOCI's exclusive compatibility with optical sensors. In the response-phase task, the ranking of sensors based on the DOCI is same as that of the OCEM, demonstrating that the OCEM can substantially fulfill the role of the DOCI. Conversely, differences emerge in the preparedness-phase task when comparing the rankings depicted in Figure 7. While the DOCI-based sensor ranking resembles OCEM's, specific discrepancies are notable. For instance, the GIS sensor from GeoEye-1 ranks second in the OCEM but fifth in the DOCI, while the Multi-Spectral Camera sensor from Komspat-2 holds the second position in the DOCI but falls to fifth in the OCEM. Analyzing Table 5, the GIS's EnIm surpasses that of the Multi-Spectral Camera by 9%, although the latter exhibits a 2% higher SpCo. Furthermore, despite the GIS's ReTi being 30% higher than that of the Multi-Spectral Camera, this factor is of lesser importance. Thus, the GIS sensor is favored due to its broader valid spatial coverage, aligning with the spatiotemporal requirements of the preparedness-phase task and affirming the OCEM's accuracy. Moving to the recovery-phase task comparison in Figure 6, disparities arise between the top-ranking optical sensors. HiRAIS from DubaiSat-2 secures the top spot in the OCEM but ranks third in the DOCI, whereas REIS from RapidEye-2 claims the first position in the DOCI but drops to seventh in the OCEM. Examining Table 7, while the SpCo, ThCo, and EnIm values for HiRAIS and REIS are largely similar, the significant difference lies in their SpaRes values. HiRAIS with a SpaRes value of 1 outweighs REIS, which registers a lower value of 0.1538. Notably, the recovery-phase task favors sensors capable of obtaining high-quality observation data, particularly with higher spatial resolution, reinforcing the OCEM's accuracy over the DOCI. Table 11 illustrates the qualitative comparative results between the OCEM and seven other methods, including the DOCI, sensor static capability index (SSCI) [25], sensor observation capability association ontology (SOCA Ontology) [58], sensor observation capability object field (SOCO-Field) [15], star sensor observation capability evaluation model (SSOCEM) [32], Earth observation potential evaluation model (EOPEM) [33], and sensor band selection method (SBSM) [11]. An overview of these methods is included in the final column of the table. Analyzing the \"Evaluation Mode,\" it is evident that SOCA Ontology and SOCO-Field solely support qualitative evaluation, lacking the ability to rank sensors for optimal selection and planning. In contrast, the OCEM, DOCI, SSCI, SSOCEM, EOPEM, and SBSM enable quantitative evaluation, providing support for optimal sensor selection and planning. Furthermore, considering the \"Applicable Scenario\" and \"Spatiotemporal Characteristics Considered,\" the OCEM stands out as the only model capable of simultaneously considering the spatiotemporal characteristics of an observation scenario (dynamic flood process and real-time environmental impacts on sensors), a feature lacking in other methods. Based on the comprehensive quantitative and qualitative comparative analysis, it becomes evident that the OCEM excels in quantifying sensor observation capability with superior accuracy, also extending support for evaluating microwave sensors.