Effectiveness evaluation of multi-satellite joint coverage data for large area targets based on cloud prediction information

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.