With the development of large-scale commercial remote sensing Low Earth Orbit Satellite Constellations, satellites are capable to work in relay observation mode over the same ground area within a shorter period, which enables continuous monitoring of aircraft targets at airports. This paper proposes a two-stage aircraft target tracking method based on YOLOv7 and DeepSORT for airport aircraft target tracking under joint monitoring of low orbit satellites. By introducing an attention mechanism to improve the YOLOv7 network, accurate detection of small aircraft targets in satellite remote sensing images is achieved; By optimizing the matching mechanism and improving the DeepSORT algorithm, continuous tracking of moving aircraft targets in image sequences with long time differences can be achieved. This article uses satellite images sequences captured by multiple Jilin-1 satellites for algorithm validation. The experimental results show that the method proposed in this paper can integrate imaging information from multiple satellites at different times, improve the continuous perception ability of airport operation status, and achieve a multi-target tracking accuracy of 79.15%, which is more than 3.2% higher than other commonly used tracking algorithms. At the same time, it shows the significant application potential of large-scale low-Earth orbit commercial remote sensing satellite constellations in the field of objects tracking.