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
Authors: Balde Hamadou , Hmimina Gabriel , Goulas Yves , Latouche Gwendal , Soudani Kamel 
Published: April 2023

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 and ecosystem
functioning, as well as ecosystem responses and adaptations to climate
change. Prior 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 types (PFTs). However, questions remain regarding
whether there is a unique relationship between SIF and GPP across different
sites and PFTs and how we can improve GPP estimates using solely remotely
sensed data. Using concurrent measurements of daily TROPOspheric
Monitoring Instrument (TROPOMI) SIF (daily SIFd); daily MODIS Terra and Aqua spectral
reflectance; vegetation indices (VIs, notably normalized
difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv),
and photochemical reflectance index (PRI)); and daily tower-based GPP across
eight major different PFTs, including mixed forests, deciduous broadleaf
forests, croplands, evergreen broadleaf forests, evergreen needleleaf
forests, grasslands, open shrubland, and wetlands, the strength of the
relationships between tower-based GPP and SIFd at 40 Integrated
Carbon Observation System (ICOS) flux sites was investigated. The synergy between
SIFd and MODIS-based reflectance (R) and VIs to improve GPP estimates
using a data-driven modeling approach was also evaluated. The results
revealed that the strength of the hyperbolic relationship between GPP and
SIFd was strongly site-specific and PFT-dependent. Furthermore, the
generalized linear model (GLM), fitted between SIFd, GPP, and site and
vegetation type as categorical variables, further supported this site- and
PFT-dependent relationship between GPP and SIFd. Using random forest (RF)
regression models with GPP as output and the aforementioned variables
as predictors (R, SIFd, and VIs), this study also showed that the
spectral reflectance bands (RF-R) and SIFd plus spectral reflectance
(RF-SIF-R) models explained over 80 % of the seasonal and interannual
variations in GPP, whereas the SIFd plus VI (RF-SIF-VI) model
reproduced only 75 % of the tower-based GPP variance. In addition, the
relative variable importance of predictors of GPP 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 spectral reflectance and SIFd to improve
estimates of GPP across sites and PFTs.