Estimating high spatiotemporal terrestrial carbon flux using geostationary and polar-orbiting satellites: CArbon Simulator from Space (CASS)

Quantitative assessment of the carbon cycle for terrestrial ecosystem is significant to improve our understanding on climate change, as it absorbs about 30% of annual global anthropogenic CO₂ emission. To refine the carbon flux estimation, we construct a data-based model named CArbon Simulator from Space (CASS), motivated by the representative Light Use Efficiency (LUE) model VPRM. The model simply estimates carbon flux with the information of air temperature, relative humidity, photosynthetically active radiation (PAR), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI). CASS construct the hourly NEP dataset in 250m resolution for the Seoul Metropolitan Area using refined datasets, including PAR from the HIMAWARI8, geostationary satellite. Notably, CASS does not have Plant function type (PFT) dependency by replacing empirical coefficients with the machine learning regressor. The result confirms the increased ability to capture the spatiotemporal variation at local scale for NEP especially in the urban area. Our refined estimation of carbon flux is expected to help understanding the role of terrestrial ecosystem in climate crsis.