Carbon dioxide (CO₂) is the most significant greenhouse gas driving climate change.
The total amount of CO₂ fixed by terrestrial vegetation per unit time and surface area depends on the photosynthetic rate. In recent years, studies have shown that global estimates of vegetation solar-induced chlorophyll fluorescence (SIF) can potentially be used to track the dynamic behavior of photosynthesis in terrestrial ecosystems.
The goal of this project is to advance methods for estimating global photosynthetic rates by improving the accuracy of satellite-based SIF retrievals. These approaches will incorporate modern inverse problems, machine learning, and advanced post-processing techniques. In this work, we will use freely available satellite data, including data from the upcoming FLEX mission, along with our in-house SIF retrieval algorithm, SIFFI.
