Develop a high spatiotemporal resolution canopy-emitted solar-induced chlorophyll fluorescence dataset for better estimating ecosystems gross primary productivity

The recent emergence of remotely-sensed Solar-Induced chlorophyll Fluorescence (SIF) represents a major breakthrough in understanding, monitoring and quantifying global carbon cycle variabilities. However, the existing spaceborne SIF products are typically noisy, coarse and sparse in both time and space coverage, thus are not suitable for biome specific and regional carbon cycle studies. In this study, we take advantage of the complementary characteristics of the Global Ozone Monitoring Experiment (GOME-2) SIF and MODIS observations of terrestrial ecosystems to downscale the coarse-resolution SIF products into finer spatial and temporal resolutions, using an innovative Adaptive Spatial Average Ensemble Kalman Filter (ASA-EnKF). We used a simplified version of the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model and the raw level-2 GOME-2 SIF product in the data assimilation framework, and accounted for the effects of viewing geometry with a satellite-based escape ratio for SIF to develop high-resolution (i.e., 1km, hourly, gap-free) canopy-emitted SIF over the Southwest region of United States. We found the new SIF dataset to be in better agreement with independent estimates of terrestrial ecosystems gross primary productivity (GPP), thus potentially suitable for upscaling site-level GPP estimates to regional, and global scales.