Hyperspectral indices and global fluorescence: how PACE vegetation indices correlate with terrestrial photosynthesis

Solar-induced chlorophyll fluorescence (SIF) is a direct remotely sensed indicator of photosynthetic activity and plant physiological status. However, its application is limited by coarse spatial resolution and data gaps. The NASA PACE mission offers hyperspectral observations that may help address these limitations through its Ocean Color Instrument and associated Land Vegetation Index (LANDVI) products. This study evaluates the strength of the relationship between PACE-derived vegetation indices and TROPOMI SIF across diverse global biomes. Using 8-day global composites from 2024, we demonstrate strong spatial and temporal correlations between PACE vegetation indices and TROPOMI SIF across fifteen biome-stratified regions, including forests, grasslands, agricultural areas, and xeric landscapes. Even in the absence of photosynthetically active radiation (PAR) and albedo corrections, simple univariate linear models show that the Enhanced Vegetation Index (EVI) and Chlorophyll Index Red Edge (CIRE) explain a large fraction of SIF variance ( R 2 = 0.80 and 0.77, respectively). The stability of these relationships across seasons highlights the key role of canopy structure and chlorophyll content in driving global SIF variability. Seasonal analyses further reveal that while EVI and CIRE perform robustly in most forests and agricultural systems, other moisture- and pigment-sensitive indices can provide better performance during dry seasons in water-limited ecosystems. Spatial residual analyses indicate minimal global bias, though systematic deviations occur in certain regions (e.g., boreal forests and parts of the tropics), consistent with known effects of canopy architecture and fluorescence escape probability. These strong correlations suggest that PACE vegetation indices capture key biophysical drivers of SIF and point to the potential for improved SIF prediction and downscaling in future studies, particularly when combined with additional variables such as PAR corrections, albedo, or escape probability factors. Given the availability of EVI from moderate-resolution sensors (e.g., Sentinel-2 and Landsat), such relationships also offer promising avenues for bridging coarse-resolution SIF to finer management-relevant scales.