Knowledge of the status (position and speed) of flying aircraft is vital for efficient and safe air space management. However, this requirement is often compromised due to the complexity of the aviation environment. Satellite remote sensing (RS) provides a complementary means for tracing aircraft, but is often limited to finding motionless aircraft under specific scenarios (e.g., at airports). Here, based on the inter-band offsets due to hardware parallax in push-broom sensors (e.g., Sentinel-2 Multispectral Instrument or MSI), we develop a method for detecting flying aircraft in an automated fashion. Supported by a hybrid computation framework (based on Google Earth Engine computation and local computation) specifically designed to address the challenge of processing large volume of moderate resolution RS data at a global scale, the method is applied to more than 2.31 million MSI images to establish a map of the global distribution of flying aircraft. The detected flying aircraft coincide well with those determined using traditional techniques (e.g., Flightradar24), when both datasets co-exist. With the existing and future moderate-resolution data captured by push-broom satellite sensors, the method is believed to provide a robust and cost-effective means of detecting aircraft status at a global scale, thus supplementing the traditional methods for tracking flying aircraft. The same method is also used to estimate the inter-band and inter-granule time offsets in multi-band MSI and Landsat-8 Operational Land Imager (OLI) images, which may provide critical information needed to correct artifacts in aquatic applications.