Publications

Chen, XA; Huang, YF; Nie, C; Zhang, S; Wang, GQ; Chen, SL; Chen, ZC (2022). A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms. SCIENTIFIC DATA, 9(1), 427.

Abstract
Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. This study uses machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001-2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05 degrees 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R-2 = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and 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 gross 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.

DOI:
10.1038/s41597-022-01520-1

ISSN:
2052-4463