Modeling global vegetation processes and hyperspectral canopy radiative transfer using CliMA Land

Recent progress in satellite observations has provided unprecedented opportunities to monitor vegetation activity at global scale. However, a major challenge in fully utilizing remotely sensed data to constrain land surface models (LSMs) lies in inconsistencies between simulated and observed quantities. For example, gross primary productivity (GPP) and transpiration (T) that traditional LSMs simulate are not directly measurable from space, although they can be inferred from spaceborne observations using assumptions that are inconsistent with those LSMs. In comparison, canopy reflectance and fluorescence spectra that satellites can detect are not modeled by traditional LSMs. To bridge these quantities, we presented an overview of the next generation land model developed within the Climate Modeling Alliance (CliMA), and simulated global GPP, T, and hyperspectral canopy radiative transfer (RT; 400--2500 nm for reflectance, 640--850 nm for fluorescence) at hourly time step and 1 degree spatially resolution using CliMA Land. CliMA Land predicts vegetation indices and outgoing radiances, including solar-induced chlorophyll fluorescence (SIF), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near infrared reflectance of vegetation (NIRv) for any given sun-sensor geometry. The modeled spatial patterns of CliMA Land GPP, T, SIF, NDVI, EVI, and NIRv correlate significantly with existing data-driven products (mean R² = 0.777 for 9 products). CliMA Land would be also useful in high temporal resolution simulations, e.g., providing insights into when GPP, SIF, and NIRv diverge.