First quantification of atmospheric carbon dioxide from Sentinel-2 using deep learning
Abstract
The need for characterizing global atmospheric carbon dioxide (CO2) concentrations at high spatial and temporal resolution is quickly increasing. Understanding the spatial and temporal variability of CO2 would provide the necessary constraints for quantifying anthropogenic CO2 emissions as well as natural carbon sources and sinks. Both of these are becoming increasingly important as the stability of natural sinks is increasingly uncertain and as the need to verify the effectiveness of climate mitigation efforts increases. The current generation of space-based sensors, however, can only provide relatively sparse observations in space and/or in time. While upcoming missions may address some of these challenges, most are still years away from launch. This challenge has fueled interest in the potential use of data from existing missions originally developed for other applications for inferring global greenhouse gas variability.
Sentinel-2 is a European wide-swath, high-resolution, multi-spectral imaging mission consisting of two satellites launched in 2015 and 2017 and may provide an important window into this problem given its high spatial resolution combined with its full spatial coverage over global lands (with a 5-day sun-synchronous revisit time). Sentinel-2 carries an optical instrument payload that samples 13 spectral bands including visible and near-infra-red (VNIR) and short wave infra-red (SWIR) bands.
Recent work has shown the potential for using data from Sentinel-2 to detect large point-source methane emissions. The use of vegetation indices derived from Sentinel-2 observations for monitoring terrestrial net ecosystem exchange and gross primary productivity have also been proposed. Here we explore the feasibility of deriving an atmospheric CO2 signal from Sentinel-2 observations. To do so, we train a U-Net styled deep neural network which takes all the Sentinel-2 bands from a short time series of Sentinel-2 imagery as the input and predicts a CO2 enhancement per pixel at 10m resolution. In order to train and validate the model, we generate synthetic CO2 plumes using an atmospheric model and embed these plumes into the Sentinel-2 image using Beer-Lambert radiative transfer at the current timestamp. The embedded CO2 plume enhancement in ppm is used as a target for the model. We also test our approach by looking at model predictions in regions where power plants are located and verifying plumes predicted by looking at chimney locations and wind velocities. In upcoming work, we will track improvements in the algorithm against observations from the Total Carbon Column Observing Network (TCCON), ACT-America and AirCore.
Sentinel-2 is a European wide-swath, high-resolution, multi-spectral imaging mission consisting of two satellites launched in 2015 and 2017 and may provide an important window into this problem given its high spatial resolution combined with its full spatial coverage over global lands (with a 5-day sun-synchronous revisit time). Sentinel-2 carries an optical instrument payload that samples 13 spectral bands including visible and near-infra-red (VNIR) and short wave infra-red (SWIR) bands.
Recent work has shown the potential for using data from Sentinel-2 to detect large point-source methane emissions. The use of vegetation indices derived from Sentinel-2 observations for monitoring terrestrial net ecosystem exchange and gross primary productivity have also been proposed. Here we explore the feasibility of deriving an atmospheric CO2 signal from Sentinel-2 observations. To do so, we train a U-Net styled deep neural network which takes all the Sentinel-2 bands from a short time series of Sentinel-2 imagery as the input and predicts a CO2 enhancement per pixel at 10m resolution. In order to train and validate the model, we generate synthetic CO2 plumes using an atmospheric model and embed these plumes into the Sentinel-2 image using Beer-Lambert radiative transfer at the current timestamp. The embedded CO2 plume enhancement in ppm is used as a target for the model. We also test our approach by looking at model predictions in regions where power plants are located and verifying plumes predicted by looking at chimney locations and wind velocities. In upcoming work, we will track improvements in the algorithm against observations from the Total Carbon Column Observing Network (TCCON), ACT-America and AirCore.
