09 Sep 2022
09 Sep 2022

Synergy between TROPOMI sun-induced chlorophyll fluorescence and MODIS spectral reflectance for understanding the dynamics of gross primary productivity at integrated carbon observatory system (ICOS) ecosystem flux sites

Hamadou Balde1,2, Gabriel Hmimina1, Yves Goulas1, Gwendal Latouche2, and Kamel Soudani2 Hamadou Balde et al.
  • 1Laboratoire de Météorologie Dynamique, Sorbonne Université, IPSL, CNRS/Ecole Polythétique, 91128, Palaiseau Cedex, France
  • 2Ecologie Systématique et Evolution, Université Paris-Saclay, CNRS, AgroParisTech, 91190, Gif-sur-Yvette, France

Abstract. An accurate estimation of vegetation Gross Primary Productivity (GPP), which is the amount of carbon taken up by vegetation through photosynthesis for a given time and area, is critical for understanding terrestrial-atmosphere CO2 exchange processes, ecosystem functioning, and as well as ecosystem responses and adaptations to climate change. Earliest studies, based on ground, airborne and satellite Sun-Induced chlorophyll Fluorescence (SIF) observations have recently revealed close relationships with GPP at different spatial and temporal scales and across different plant functional type (PFT). However, questions remain regarding whether there is a unique relationship between SIF and GPP across different sites and PFT and how can we improve GPP estimates using solely remotely sensed data. Using concurrent measurements of daily TROPOMI (TROPOspheric Monitoring Instrument) SIF (daily SIFd), daily MODIS Terra and Aqua spectral reflectance, and vegetation indices (VIs, notably NDVI (normalized difference vegetation index), NIRv (near-infrared reflectance of vegetation) and PRI (photochemical reflectance index)) and daily tower-based GPP across eight major different PFT, including mixed forests, deciduous broadleaf forests, croplands, evergreen broadleaf forests, evergreen needleleaf forests, grassland, open shrubland, and wetland, the strength of the linear relationships between tower-based GPP and SIFd at 40 ICOS (Integrated Carbon Observation Systems) flux sites was investigated, and the synergy between these variables to improve GPP estimates using a data-driven modelling approach was evaluated. The results revealed that the strength of the linear relationship between GPP and SIFd was strongly site-specific and PFT-dependent. Furthermore, the GLM (Generalized Linear Model) model, fitted between SIFd, GPP, site and vegetation type as categorical variables, further supported this site-and PFT-dependent relationship between GPP and SIFd. This study also showed that the spectral reflectance bands (RF-R), SIFd plus spectral reflectance (RF-SIF-R) models explained over 80 % of the seasonal and interannual variations in GPP, whereas the SIFd plus VIs (RF-SIF-VI) model reproduced only 75 % of the tower-based GPP variance. In addition, the relative importance results demonstrated that the spectral reflectance bands in the near-infrared, red and SIFd appeared as the most influential and dominant factors determining GPP predictions, indicating the importance of canopy structure, biochemical properties and vegetation functioning on GPP estimates. Overall, this study provides insights into understanding the strength of the relationships between GPP and SIF and the use of the spectral reflectance and SIFd to improve GPP across sites and PFT.

Hamadou Balde et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-640', Mukund Palat Rao, 07 Oct 2022
    • AC1: 'Reply on RC1', Hamadou Balde, 18 Nov 2022
  • RC2: 'Comment on egusphere-2022-640', Anonymous Referee #1, 31 Oct 2022
    • AC2: 'Reply on RC2', Hamadou Balde, 18 Nov 2022

Hamadou Balde et al.


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Short summary
This study focuses on the relationship between Sun Induced chlorophyll Fluorescence (SIF) and ecosystem gross primary productivity (GPP), across the ICOS European flux tower network. It shows that SIF, coupled with reflectance observations, explained over 80 % of the GPP variability across diverse ecosystems, but fails to bring new information compared to reflectance alone at coarse spatial scales (~5 km). These findings have applications in agriculture, and ecophysiological studies.