Journal
SCIENTIFIC DATA
Volume 9, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41597-022-01520-1
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Funding
- National Natural Science Foundation of China [91847301, 51809007]
- Central Funds Guiding the Local Science and Technology Development of Qinghai Province [2021ZY024]
- Major Basic Research Development Program of the Science and Technology Agent, Qinghai Province [2019-SF-146]
- State Key Laboratory of Hydroscience [2019-KY-01]
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This study uses machine learning to reconstruct the satellite-observed solar-induced chlorophyll fluorescence (SIF) dataset and demonstrates its accuracy and feasibility through validation. This new dataset is valuable for assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.
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.
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