Research News

Solar-induced Chlorophyll Fluorescence Observations from Satellite Improves Global Plant Productivity Estimation

Mar 26, 2024

A recent study published in the Journal of Remote Sensing on Feb.13 introduced a new method for estimating plant productivity. Researchers from the Aerospace Information Research Institute (AIR) under the Chinese Academy of Sciences (CAS) developed a dataset called CMLR GPP (Canopy-scale Mechanistic Light Reaction Gross Primary Production) with an exceptional spatial resolution of 0.05 degree. This dataset offers estimates of gross primary production (GPP) spanning from May 2018 to December 2021 on a global scale. 

In this study, scientists utilize solar-induced chlorophyll fluorescence (SIF) observations from the TROPOMI satellite instrument to estimate how efficiently plants are converting sunlight into energy. 

With this dataset, scientists can more precisely gauge how productive plants are around the world, which is crucial for assessing agricultural yields, monitoring ecosystem health, and understanding the Earth's carbon cycle.

Previously, the availability of comprehensive global datasets directly incorporating Solar-Induced Fluorescence (SIF) has been lacking, thereby impeding the accurate estimation of plant productivity. Scientists have long relied on various indicators to gauge how much carbon dioxide is absorbed through photosynthesis, a key process in the Earth's carbon cycle. One promising indicator is SIF, which is emitted by plants during photosynthesis. This fluorescence is closely related to GPP, which is the total amount of carbon dioxide fixed by land plants per unit time through the photosynthetic reduction of CO2 into organic compounds.

To generate this dataset, scientists employed a modified mechanistic light response model at the canopy scale, capturing the intricate biological and physical processes involved in photosynthesis. 

Crucially, the model needs data on the portion of photosystem II reaction centers that are active in the canopy (canopy qL). This information is obtained using a machine learning technique known as random forest modeling. 

When comparing the estimated plant productivity (GPP) with measurements taken from towers, there was a strong correlation of 0.72, indicating a good model performance.

Furthermore, the CMLR GPP dataset demonstrated comparable performance to other established global datasets such as BEPS GPP, FluxSat GPP, and GOSIF GPP.

Prof. LIU Liangyun, who led the study, highlighted a notable aspect of the CMLR GPP dataset: its high consistency with ground measurements, particularly evident in forested and sparsely vegetated regions, setting it apart from numerous previous GPP products.   

The new dataset is expected to help scientists better measure how well plants are growing and functioning on a global level, providing valuable insights into the health of our planet's ecosystems.