A long-term reconstructed TROPOMI solar-induced fluorescence dataset using machine learning algorithms.

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° 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R² = 0.907, regression slope = 1.001). The RTSIF dataset is

SUBMITTER: Chen X 

PROVIDER: S-EPMC9300726 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature