Optimal selection of satellite XCO ₂ images for urban CO ₂ emission monitoring

Abstract. There is a growing interest in estimating urban CO2 emission from spaceborne imagery of the CO2 column-average dry-air mole fraction (XCO2). Emission estimation methods have been widely tested and applied to actual or synthetic images. However, there is still a lack of objective criteria for selecting images that are worth processing. This study analyzes the performances of an automated method for estimating urban emissions as a function of the targeted cities and of the atmospheric conditions. It uses synthetic data experiments with synthetic truth and 9920 synthetic satellite images of XCO2 over 31 of the largest cities across the world generated with a global adaptive-mesh model, the Ocean–Land–Atmosphere Model (OLAM), zoomed in at high resolution over these cities. We use a decision tree learning method applied to this ensemble of synthetic images to define criteria based on these emission and atmospheric conditions for the selection of suitable satellite images. We show that our automated method for the emission estimation, based on a Gaussian plume model, manages to produce estimates for 92 % of the synthetic images. Our learning method identifies two criteria, the wind direction's spatial variability and the targeted city's emission budget, that discriminate images whose processing yields reasonable emission estimates from those whose processing yields large errors. Images corresponding to low spatial variability in wind direction (less than 12°) and to high urban emissions (greater than 2.1 kt CO2 h−1) account for 47 % of the images, and their processing yields relative errors in the emission estimates with a median value of −7 % and an interquartile range (IQR) of 56 %. Images corresponding to a high spatial variability in wind direction or to low urban emissions account for 53 % of our images, and their processing yield relative errors in the emission estimates with a median value of −31 % and an IQR of 99 %. Despite such efficient filtering, the accuracy of the estimates corresponding to the former group of images varies widely from city to city.