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

Xingan Chen; Yuefei Huang; Chong Nie; Shuo Zhang; Guangqian Wang; Shiliu Chen; Zhichao Chen

doi:10.1038/s41597-022-01520-1

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

Sci Data. 2022 Jul 20;9(1):427. doi: 10.1038/s41597-022-01520-1.

Authors

Xingan Chen¹, Yuefei Huang^{1

2

3}, Chong Nie^{4

5}, Shuo Zhang⁶, Guangqian Wang¹, Shiliu Chen¹, Zhichao Chen¹

Affiliations

¹ State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China.
² The Key Laboratory of Ecological Protection and High Quality Development in the Upper Yellow River, Qinghai Province, China.
³ State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining, Qinghai, 810016, China.
⁴ Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
⁵ National Joint Research Center for Yangtze River Conservation, Beijing, 100012, China.
⁶ State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China. zhangs2019@tsinghua.edu.cn.

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 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.

Publication types

Research Support, Non-U.S. Gov't

Abstract

Publication types

Grants and funding