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Research article 22 Jun 2018

Research article | 22 Jun 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Biogeosciences (BG).

A global spatially Continuous Solar Induced Fluorescence (CSIF) dataset using neural networks

Yao Zhang1, Joanna Joiner2, Seyed Hamed Alemohammad3, Sha Zhou1, and Pierre Gentine1,4 Yao Zhang et al.
  • 1Department of Earth and Environmental Engineering, Columbia University, New York, NY 10027, USA
  • 2NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
  • 3Radiant.Earth, Washington, D.C. 20005, USA
  • 4Earth Institute, Columbia University, New York, NY 10027, USA

Abstract. Satellite-retrieved Solar Induced Chlorophyll Fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurements footprints also hinder the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially continuous SIF (CSIF) datasets at moderate spatio-temporal resolutions (0.05 degree 4-day) during 2001–2016, one for clear-sky conditions and the other one in all-sky conditions. The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39%) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with gross primary production (GPP) estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of GPP-SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two continuous SIF datasets and the derived GPP-SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

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Yao Zhang et al.
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Yao Zhang et al.
Data sets

CSIF Y. Zhang, J. Joiner, S. H. Alemohammad, S. Zhou, and P. Gentine https://doi.org/10.6084/m9.figshare.6387494

Model code and software

Continuous_SIF Y. Zhang, J. Joiner, S. H. Alemohammad, S. Zhou, and P. Gentine https://doi.org/10.5281/zenodo.1294315

Yao Zhang et al.
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Short summary
Using satellite reflectance measurements and a machine learning algorithm, we generated a new solar induced chlorophyll fluorescence (SIF) dataset that is closely linked to plant photosynthesis. This new dataset has higher spatial and temporal resolutions, and lower uncertainty compared to the existing satellite retrievals. We also demonstrated its application in monitoring drought and improving the understanding of SIF photosynthesis relationship.
Using satellite reflectance measurements and a machine learning algorithm, we generated a new...