A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms - Xingan Chen (11334186) (2025)
Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in measuring SIF, but the short temporal coverage of the data records has limited its applications in long-term studies. This dataset uses machine learning to reconstruct TROPOMI SIF (RTSIF) for 2001-2020 with a spatial resolution of 0.05° and a temporal resolution of 8 days. Our machine learning model has high accuracy on the training and testing data (R 2 = 0.907, regression slope = 1.001). The RTSIF dataset is in good agreement with the original TROPOMI SIF, and its accuracy is further validated against tower-based SIF. The RTSIF dataset is also compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.