The mass of an aircraft is crucial for performance-related studies, such as predicting flight trajectories and analyzing flight emissions. In these studies, the flight trajectories are often reconstructed using a point-mass aircraft performance model combined with flight profiles from surveillance data and take-off mass information. However, airlines do not usually disclose take-off mass information, considering its sensitive nature. Thus, aircraft masses often need to be assumed or estimated. This paper presents a simple and computationally effective approach for estimating take-off mass using only open data and models. We explore the strong correlation between take-off mass, flight distance, cruise altitude, and partially, the airspeed during the cruise. The main idea is to generate fuel-optimal trajectories with known masses and distances, and then compare them with actual flight data. The optimal trajectories are generated using the open aircraft performance and optimization library. By assuming that actual flights follow quasi-fuel-optimal trajectories, the take-off mass of a flight can be estimated based on simple regression models trained on the optimal trajectory dataset. This open-loop take-off mass estimation approach requires no proprietary information from aircraft manufacturers or airlines. We verified the model with an anonymized dataset containing actual A320 flights with known take-off mass. Our two- and three-feature multi-linear models yield mean absolute percentage errors of 5.95 % and 4.89 %, respectively. This study is another step forward in open science and a contribution to the aircraft trajectory studies.

The increasing demand for global air travel has intensified the urgency to mitigate aviation’s carbon emissions. Continuous monitoring of aircraft fuel efficiency and emissions has become an important task in aviation. One of the main challenges has been the lack of surveillance data for flights across oceans, specifically in the North Atlantic region, where numerous flights occur. Recently, space-based ADS-B data has been made available by new space companies like Spire Global, enabling flight surveillance for aircraft in remote regions, including transatlantic flights. In this study, we utilize several months of space-based ADS-B data from Spire, combined with groundbased ADS-B data from the OpenSky Network, to demonstrate increased accuracy in flight trajectory and emission estimations. We introduce the use of wind data to improve emission quantification. Utilizing these accurate trajectories, we quantify excess emissions by comparing actual flight paths with their optimal alternatives. Our approach provides a robust methodology that benefits future policy for carbon emissions assessments.