The number of remote sensing satellites is increasing, and the types and number of payload are also increasing. User demand is also increasing, and satellite resources are still relatively tight. To study how to coordinate the joint observation of multiple satellites and multiple payloads, give full play to the overall observation benefits of remote sensing satellite resources, and meet the increasingly complex task requirements of various users, all available resources must be integrated. Realize the overall management of remote sensing satellite resources and task collaborative planning, and improve the ability of data acquisition and support through the effective integration of various resources. Research on intelligent task planning technology based on multi satellite and multi load. Through the research and construction of satellite and ground resource models, and standardized requirements and task models, the constraints of intelligent satellite task planning are realized, and the optimal scheduling under conventional planning is realized. Research and build the multi-objective optimization planning model of resource observation to improve the intelligence of task planning algorithm. Scientifically arrange satellite imaging and give full play to the efficiency of satellite use.
Optical satellite imaging is easy to be affected by weather, and imaging in the area with high cloud cover will result in poor quality or even complete unavailability of satellite imaging products. Addressing the lack of effective methods for evaluating data acquisition in large-area, multi-satellite joint observations, this paper utilizes the VIRR global cloud daily product data from the FY-3A/3C meteorological satellites spanning 2010 to 2020. This data is analyzed to extract cloud amount information, subsequently used to obtain the regional clear sky index sequence. The SARIMA time series prediction model is then employed to forecast the clear sky index of the observation area effectively. This forecast is combined with satellite transit information to establish a data acquisition validity prediction model. Utilizing the predicted monthly clear sky index for Beijing in 2021, alongside the satellite transit information for each period, the model predicts data validity. The average discrepancy between the predicted and actual values is 0.36. This data validity prediction model can identify optimal observation periods for multi-satellite joint coverage of large-area targets and provide effective data support for coverage prediction within the observation period.
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