# Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection
## 5 Conclusions and Future Research  
On the basis of the analysis of the spatiotemporal characteristics of floods and the multidimensional observation capabilities of satellite sensors, this study proposes the OCEM to quantitatively pre-evaluate each sensor's observation performance, thus enabling the selection and planning of the optimum sensor for flood water monitoring. An experiment was conducted on the basis of a real flood disaster which occurred in Henan Province, China, to validate the effectiveness of the OCEM. Three distinct observation tasks were established to correspond with three flood phases: preparedness, response, and recovery. A total of 16, 15, and 14 satellite sensors were queried in relation to these respective phases. The evaluation process involved computing each sensor's observation capability factor values, assigning appropriate weights to these factors based the spatiotemporal characteristics specific to each flood phase, and calculating each sensor's OCEM value. The results show that in the preparedness, response, and recovery phases, three, two, and two microwave sensors, respectively, exhibited superior performance compared to other optical sensors. Moreover, terrible weather conditions rendered one and seven optical sensors completely ineffective, signified by an OCEM value of 0 in the preparedness and response observation tasks. In addition, quantitative and qualitative comparisons with other related methods demonstrated the superior efficacy, flexibility, and overall superiority of the OCEM in flood water observation tasks. In general, for disaster management agencies like the International Disaster Charter, which can dispatch and control satellite sensors, the OCEM can help in selecting and planning the most suitable sensors to gather real-time flood water observation data. This facilitates a more effective and reliable understanding of the flood situation and response.  
Although the OCEM has achieved a more advanced performance in flood water observation tasks, it relies heavily on expert knowledge to determine the weights of these observation capability factors due to the lack of a \"golden standard\" or \"knowledge base.\" Therefore, future research will concentrate on utilizing the knowledge graph technology [59] to construct a sensor observation capability evaluation-oriented knowledge graph, which is expected to support the sensor planner to set the weights of various factors in a guided and intelligent manner.  
Conceptualization, M.D., Y.Z. and P.Y.; methodology, M.D., Y.Z. and J.L.; software, R.L. and S.C.; validation, G.D., X.Y. and P.Y.; formal analysis, M.D., X.Y. and P.Y.; investigation, R.L.; data curation, G.D. and P.Y.; visualization, R.L. and S.C.; supervision, J.L.; writing--original draft preparation, M.D.; writing--review and editing, Y.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (NFSC) Program (Grant No.: 42071380), the Special Fund of Hubei Luojia Laboratory (Grant No.: 220100034), and the Open Fund of Hubei Luojia Laboratory (Grant No.: 220100056).  
Not applicable.