20 January 2025 Global continuous solar-induced chlorophyll fluorescence product from 2000 to 2021 using multivariate OCO-3 data and MODIS
Shiyan Sun, Yao Huang, Rui Wang, Yanan Xie
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Abstract

In recent years, solar-induced chlorophyll fluorescence (SIF) has been widely studied as an ideal “probe” to directly monitor vegetation photosynthesis. Satellite-retrieved SIF shows great potential for estimating gross primary productivity (GPP), but discontinuous space coverage limits its application. Although there are now some products to reconstruct satellite-scale SIF, it is mainly focused on established carbon satellites such as Orbiting Carbon Observatory-2 (OCO-2) and Tropospheric Monitoring Instrument (TROPOMI). Orbiting Carbon Observatory-3 (OCO-3), a carbon satellite launched by NASA in 2019, has more flexibility and observability than OCO-2, but there are still fewer SIF products for OCO-3. We generated a global contiguous SIF product SIFxco2 (8 days, 0.05 deg) over the period 2000 to 2021 from multivariate OCO-3 data and MODIS. The predictive model is developed by CatBoost. We innovatively added column-averaged atmospheric carbon dioxide concentrations (XCO2) as an explanatory variable, which is usually inversely related to SIF, to improve the model’s SIF explanation performance. SIFxco2 obtains good performance (R2=0.84, RMSE = 0.062) in estimating OCO-3 SIF, and it also correlates well with the flux tower GPP (R2=0.746). In addition, it accurately responds to the temporal and spatial variations of level 2 SIF on a global scale. Our SIFxco2 product holds value in evaluating terrestrial photosynthesis and ecosystem functioning, as well as serving as a benchmark for terrestrial biosphere and Earth system models.

© 2025 Society of Photo-Optical Instrumentation Engineers (SPIE)

Funding Statement

Shiyan Sun, Yao Huang, Rui Wang, and Yanan Xie "Global continuous solar-induced chlorophyll fluorescence product from 2000 to 2021 using multivariate OCO-3 data and MODIS," Journal of Applied Remote Sensing 19(1), 018501 (20 January 2025). https://doi.org/10.1117/1.JRS.19.018501
Received: 13 September 2024; Accepted: 30 December 2024; Published: 20 January 2025
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KEYWORDS
Education and training

Data modeling

Fluorescence

Chlorophyll

Satellites

Machine learning

Reflectivity

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