The TANGO mission represents a significant leap forward in the global monitoring of greenhouse gas emission plumes, particularly CO2 and NO2. Comprising two satellites, TANGO-Carbon and TANGO-Nitro, the mission is designed to operate from 2027 to 2031, offering unprecedented capabilities through stereo (temporally separated) and high spatial res- olution emission plume images. These capabilities create a unique framework that facilitates the development of novel estimation methods for emission rates of greenhouse gas emitters. Conventional methods for estimating emission rates of CO2 and NO2 rely on combining gas concentration measurements with wind velocity fields derived from meteorological data, often introducing substantial uncertainties due to the inherent inaccuracies in wind velocity estimation. To address this challenge, this research explores alternative methods enabled by TANGO’s innovative framework, which allows for the direct measurement of emission plume velocity from concentration data. By eliminating the dependency on uncertain me- teorological inputs, these methods promise to reduce the uncertainty in emission rate estimates.
Through detailed simulations and analyses, this research demonstrates that the TANGO mission can effectively establish a framework for directly measuring emission plume velocities. By simulating the data products of the TANGO satellites using Large-Eddy Simulations and applying advanced methods such as traditional Correlation Image Velocimetry (CIV) and Computer Vision Correlation Image Velocimetry (CVision-CIV), wind velocity fields were successfully estimated from plume imagery. These estimates were found to be of promising precision across a range of conditions, including varying wind velocities, emission rates of greenhouse gasses, and levels of measurement noise in the simulations. Results revealed that the CVision-CIV method outperforms the traditional CIV method, especially in scenarios with low signal-to-noise ratios. Wind velocity fields directly estimated from plume imagery were implemented in the Cross-sectional Flux Method to estimate CO2 emission rates. The emission rate estimates indicate that direct plume velocity measure- ments provide a more accurate estimate of emission source rates than conventional methods, which rely on indirect wind velocity estimates from meteorological data. The use of wind velocity fields obtained through the CVision-CIV method resulted in CO2 emission rate estimates with ±20% accuracy in most scenarios, particularly under optimal SNR conditions. Additionally, the study highlights the impact of mission parameters such as image resolu- tion and measurement noise on the accuracy of wind velocity estimations. It was found that higher image resolution and longer time intervals between measurements enhance the precision of wind velocity field estimates by reducing the relative effects of measurement noise.
In summary, this research demonstrates that direct estimation of wind velocities from emission plume imagery, as enabled by the TANGO mission’s advanced capabilities, can accurately be performed and significantly enhance the accuracy of emission rate estimates. The improved wind velocity estimation methods proposed in this thesis offer a promising advancement in remote sensing techniques for greenhouse gas monitoring.