Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America

Abstract

Satellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet understanding of the relationship between GPP and remote sensing observations and how it changes with factors such as scale, biophysical constraint, and vegetation type remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have characteristic high spatiotemporal variability and are under‐represented by long‐term field measurements. Here we utilize an eddy covariance (EC) data synthesis for southwestern North America in an assessment of how accurately satellite‐derived vegetation proxies capture seasonal to interannual GPP dynamics across dryland gradients. We evaluate the enhanced vegetation index, solar‐induced fluorescence (SIF), and the photochemical reflectivity index. We find evidence that SIF is more accurately capturing seasonal GPP dynamics particularly for evergreen‐dominated EC sites and more accurately estimating the full magnitude of interannual GPP dynamics for all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite‐based GPP estimates.

Solar‐Induced Fluorescence Detects Interannual Variation in Gross Primary Production of Coniferous Forests in the Western United States

“…Our results corroborate previous findings that SIF is a better metric for GPP than are NDVI or EVI (Damm et al, ; Duveiller & Cescatti, ; Joiner et al, ; Smith et al, ; Yang et al, ). However, an important question remains for SIF in coniferous forests—what happens to the SIF‐GPP connection during winter dormancy associated with cold stress?…”

“…Sensor degradation has a known impact on the GOME‐2 SIF time series (Smith et al, ; Yao Zhang et al, ). However, our main findings were unchanged after detrending the SIF data (supporting information S1), suggesting that SIF may be used to detect actual variability in GPP despite sensor‐related artifacts.…”

Sun‐induced Chl fluorescence and its importance for biophysical modeling of photosynthesis based on light reactions

“…Although Eqn 3 is appealing because of its simplicity for linking SIF to GPP, it gives the disappointing impression that the only role that SIF plays is to replace the absorbed PAR ( α grn × PAR), reinforcing a view that may be drawn from first‐order analyses of the SIF–GPP relationship that cover broad spatial or temporal gradients (e.g. Smith et al ., ; Yang K. et al ., ). In doing so, it also introduces three new unknown parameters: Φ SIF , ε and β .…”

Phenology Dynamics of Dryland Ecosystems Along the North Australian Tropical Transect Revealed by Satellite Solar‐Induced Chlorophyll Fluorescence

“…Satellite SIF retrievals have been demonstrated to be highly correlated with GPP at large scale and could be used to reveal GPP dynamics in response to environmental variations (e.g., Frankenberg et al, ; Guanter et al, ; Sun et al, ). Specific to dryland systems, studies have quantified the relationship between satellite SIF and GPP (e.g., Li, Xiao, & He, ; Li, Xiao, He, Altaf Arain, et al, ; Sanders et al, ; Smith et al, ; Verma et al, ). For example, Verma et al () found a robust linear correlation between NASA (National Aeronautics and Space Administration)'s Orbiting Carbon Observatory‐2 (OCO‐2) SIF and GPP derived from an eddy covariance (EC) flux tower at a semiarid grassland site in Australia over a season.…”