Summary
In light of climate change, scaling up in situ eddy covariance (EC) fluxes with Earth observation data has been recognized as a viable strategy for estimating the global terrestrial ecosystem carbon uptake, specifically, gross primary productivity (GPP). Nevertheless, the significant uncertainty in estimation (100–150 PgCyr-1) necessitates the refinement of upscaling algorithms and the use of appropriate satellite data. This technological advancement is particularly sought after in underprivileged regions that are most susceptible to climate crises. Unfortunately, these regions are often constrained by insufficient financial resources and software engineering skills shortages. This study aims to evaluate satellite vegetation proxies [solar-induced fluorescence (SIF); near-infrared reflecta
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