Multisatellite Scheduling Method Based on a Historical Cloud Coverage at Regional Scale

Earth observation satellites are crucial in regional scale monitoring, but the effectiveness of optical satellite is often severely restricted by cloud cover. Most existing satellite scheduling methods at regional-scale ignore the impact of clouds, resulting in a waste of observation resources and degraded data quality. To address this problem, this study proposes a regional scale multi-satellite scheduling framework that considers a historical cloud cover for the first time. The core of the framework is a novel pixel-region integrated method (PRIM), which quantifies the functional relationship between observation success probability (OSP) and observation success fraction (OSF) using historical cloud products (MOD35) and satellite overpass information. We construct an optimization model to maximize OSF under a user-preset OSP (e.g., 95%) and use a genetic algorithm (GA) to solve it. Through experiments in three different climate and terrain regions in China, we compare the proposed method with two baseline strategies, namely, “nadir observation” and “maximum coverage”. The results show that the proposed method can not only achieve 100% geometric coverage, but also far exceeds the baseline strategy in terms of effectiveness in considering cloud effects. For example, at 95% OSP confidence level, the OSFs obtained by this method for the three regions are on average 14% higher than those of the maximum coverage strategy. In addition, through Monte Carlo simulation verification, the central limit theorem (CLT) approximation method we rely on improves computational efficiency by hundreds of times while ensuring accuracy. This framework can provide mission planners with decision-making solutions with clear probabilistic guarantees derived from long term historical cloud statistics, significantly improving the efficiency of satellite resource utilization and the ability to obtain effective data under the influence of cloud uncertainty.