Serie: NILU rapport 14/2022
The main goal of this feasibility study was to evaluate the potential of adding value to the Sentinel 5P TROPOMI methane product over Norway and the Arctic through the synergistic use of relevant observations from other Sentinel satellites and machine learning. We assessed the data availability of ESA operational and research-based WFMD XCH4 products over the Northern hemisphere, the Nordic countries and the Arctic/Northern latitudes. ESA’s XCH4 data have poor coverage over Norway. Seeing the two datasets as complementary, seems to be the most reasonable approach for utilization them. Furthermore, we investigated potential synergies between satellite products from different platforms. A random forest (RF) machine learning algorithm was implemented. It shows the importance of daytime land surface temperature (LST) as predictor variable for CH4. Our results indicate that the RF-model has a very good capability of filling small gaps in the data.