Seasonal patterns of canopy photosynthesis captured by remotely sensed sun-induced fluorescence and vegetation indexes in mid-to-high latitude forests: A cross-platform comparison
Graphical abstract
The original EC GPP and OCO-2 SIF with the fitted seasonal cycles and predicted SOS and EOS at six forested sites.
Introduction
Terrestrial ecosystems play an important role in regulating regional and global climate (Burrows et al., 2011). Mid-to-high latitude forests, especially the boreal forests, are substantial contributors to carbon fluxes (Beer et al., 2010; Rolleston, 1996). As plants in these regions are expected to experience the greatest warming among forest biomes, they are deemed to react and respond sensitively to climate change and variability (Keenan et al., 2014). In recent years, with the developments of networks of flux measurements and advances of remote sensing based models, the monitoring of the physiological processes such as photosynthesis of mid-to-high latitudes has become generally possible.
Mid-to-high latitude forests are showing noticeable seasonal cycles of canopy photosynthesis. These life cycle events are sensitive indicators of the biosphere's response to climate changes through contributions to the global carbon, energy and water cycles (Buitenwerf et al., 2015; Peñuelas et al., 2009). Understanding the changes of these cycles as well as the underlying mechanisms are of significance for predicting future changes of climate and the global carbon cycle. Recent in-situ and remote sensing based studies have shown that the warming climate has triggered lengthier growing seasons in northern hemisphere regions (Cleland et al., 2007; Viña et al., 2016; Wang et al., 2015). Remote sensing based approaches to estimate phenological metrics (e.g., the start and end of growing seasons) were mainly based on reflectance-calculated vegetation indexes (VIs), such as normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and leaf area index (LAI) retrieved using these VIs (Helman, 2018). These indexes have been applied to regional and global studies, especially for the regions without long-term ground observations (Gonsamo and Chen, 2016). Fundamentally, yet, those VIs cannot provide us a direct understanding of physiological processes so that can be hard to be perfectly applied to modelling frameworks. At the same time, several recent studies found that performances of VIs are significantly hindered by snow cover and soil moisture in high-latitude regions (D'Odorico et al., 2015; Peng et al., 2017; Wu et al., 2017). Several improved indexes including phenology index (PI) that aimed at the match between remotely sensed and ground observed seasonal cycles of canopy photosynthesis have been proposed in recent years (Gonsamo et al., 2012a). The PI combines NDVI and Normalized Difference Infrared Index (NDII) aiming to decouple the seasonality of the green vegetation component from the background one because green-up co-occurs with snow melt (Delbart et al., 2005; Gonsamo et al., 2012a). Yet, the biological recovery and dormancy for evergreen forests are still extremely difficult to identify during the transition period when the greenness signal of the vegetation is weak or does not necessarily correspond with the shifts of photosynthesis (Wong and Gamon, 2015).
Fortunately, recent advances of atmospheric measurements has made it possible to retrieve an alternative indicator that is more related to the photosynthesis processes: sun-induced fluorescence (SIF). Chlorophyll pigments absorb photons to power photosynthesis, with some of the photons are re-emitted at longer wavelengths as chlorophyll fluorescence (Baker, 2008). The re-emitted SIF has been successfully related to downward carbon flux, i.e., carbon uptake by the vegetation. This provides a promising way in estimating photosynthesis through SIF. Global SIF datasets using space-borne spectroscopy from satellites became available past few years (Frankenberg et al., 2011; Frankenberg et al., 2014; Guanter et al., 2013; Guanter et al., 2014; Joiner et al., 2013; Joiner et al., 2016; Köhler et al., 2015). Despite the complex processes underlying the relationship between SIF and gross primary production (GPP), it has been reported the satellite-retrieved SIF was highly correlated with GPP estimated based on eddy covariance (EC) flux towers (van der Tol et al., 2014; Verma et al., 2017; Yang et al., 2017; Yang et al., 2015; Zhang et al., 2016b). Their relationship appears to reflect the level of absorbed photosynthetically active radiation (APAR) with additional information of light use efficiency (LUE). Based on >50 EC towers, Joiner et al. (2014) found that the Global Ozone Monitoring Experiment-2 (GOME-2) SIF retrieved phenological metrics matched closely with that of EC-based estimations, although the footprints of GOME-2 (40 km by 80 km) were significantly larger than most EC sites. Walther et al. (2016) found that GOME-2 SIF decoupled growing seasons can be up to 6 weeks longer than that captured by EVI. Jeong et al. (2017) evaluated remotely sensed SIF and NDVI of several platforms and proposed that the continued measurements of SIF and NDVI would help us to understand the seasonal variations of vegetation photosynthesis and greenness. However, the coarse spatial representativeness of previous atmospheric measurements (~40 km by 80 km or coarser) makes it difficult to compare with ground-based canopy measurements (Chen et al., 2012; Joiner et al., 2014; Zhang et al., 2016b). Very recently, Orbiting Carbon Observatory 2 (OCO-2) has shown renewed promises of provinding satellite-derived SIF with the improved spatial representativeness at around 1.3 km by 2.25 km (Frankenberg et al., 2014). The footprints of OCO-2 that match the spatial representativeness of most EC towers enables it to produce better results (Lu et al., 2018; Verma et al., 2017). The emerging observations from OCO-2, however, have rarely been applied in phenological studies (Köhler et al., 2018).
In most physiological models, VIs and/or LAI were used to decouple the seasonal cycles of processes such as photosynthesis (Wang et al., 2016). To constrain the uncertainties of current models regarding the estimations of productivity, the use of high-resolution and global retrieval of SIF might further improve the accuracy. In the context of extreme events including prolonged droughts in recent decades, it was vital to comprehensively investigate the usability of SIF in monitoring canopy photosynthesis, including the key phenological metrics, compared with conventional VIs and LAI (Dahlin et al., 2015; Melaas et al., 2016; Zipper et al., 2016). At the same time, although several models that can estimate GPP globally with VIs have been proposed, the advantages of SIF can potentially be exploited to improve their performances especially at high-latitude regions (Jeong et al., 2017; Luus et al., 2017; Luus and Lin, 2015). However, the advantages of SIF compared against conventional VIs have not been comprehensively accessed in the context that there are advances of new satellites capable of monitoring SIF at relatively high spatial resolutions and proposal of improved VIs with different theories (Sun et al., 2017). In this study, our primary objective was to evaluate and compare the seasonal cycles of several remotely sensed canopy measurements across mid-to-high latitude forests with a focus on evergreen needleleaf forests (ENF), deciduous broadleaf forests (DBF) and mixed forests (MF). An additional objective was to focus on phenological transition dates derived from different platforms, which are indicators directly related to the carbon budgets of terrestrial ecosystems.
Section snippets
EC estimated canopy properties
We conducted this study at 15 EC sites in North America and Europe where relatively homogeneous landscapes exist around the flux towers. In total, there are 103 site-years with a mean of 6.9 years of observations for each site. These sites represent three main forest biomes in mid-to-high latitude forests such as ENF, DBF andMF (Fig. 1 and Table 1). The selection of EC sites was based on an assumption of threshold (60%) of International Geosphere-Biosphere Program (IGBP) classifications (
Relationship between satellite-derived SIF and EC towers based GPP
We used the OCO-2 SIF to explore the relationship of satellite-derived SIF and GPP at instantaneous scale at US-PFa and US-WCr first (Fig. 2). The EC instruments of US-PFa and US-WCr were located in a mixed forest and a deciduous forest in northern Wisconsin, USA, respectively. From late 2014 to 2017, there were 21 times of observations from OCO-2 at US-PFA and 20 times of observations at US-WCr. At both sites, the correlations of linear and hyperbolic models were statistically significant
SIF-GPP relationship
In this study, we focused on 15 EC sites in mid-to-high latitude forests to examine the seasonal trajectories of satellite-derived VIs, LAI, and SIF, as well as their relationships with EC-based estimations of canopy photosynthesis. An additional objective was to explore the capacities of five remote sensing based measurements to track the key seasonal metrics in photosynthesis.
Despite the potentials of SIF to estimate GPP at various spatial and temporal scales, the SIF-GPP relationship can be
Conclusions and outlooks
Our results added additional endorsements for the applications of satellite-derived SIF in phenological studies in forest biomes. In 15 mid-to-high latitude forests in North America and Europe, the seasonal trajectories of GOME-2 datasets were significantly correlated with GPPEC with R2 values ranged from 0.52 to 0.74 with the linear model, while that ranged from 0.54 to 0.74 with the hyperbolic model. At the same time, the start and end of growing seasons estimated by GOME-2 and OCO-2 SIF
Acknowledgments
This work was supported by the Chinese Arctic and Antarctic Administration, National Natural Science Foundation of China, the Chinese Polar Environment Comprehensive Investigation, Assessment Program (Grant No. 41676176 and No. 41676182). This work was also supported by the Fundamental Research Funds for the Central Universities (Grant No. 312231103). We acknowledge Dr. Köhler P. for sharing the satellite-derived SIF, Dr. Tramontana G. and Prof. M. Jung for providing the upscaled model GPP. The
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2022, Ecological IndicatorsCitation Excerpt :The results reveal that optimal results can be approximated when SIF and PRI are considered for estimating the assimilation rate of CO2. ( Xinchen Lu et al., 2018) reveals the pattern of seasonal variations in canopy photosynthesis using a hyperbolic model (Non-Linear). Several studies show that the SIF-GPP relationship is not linear, so the LUE can be correlated with PAR using a hyperbolic function.
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2022, Ecological IndicatorsCitation Excerpt :The mounting evidences indicated that SIF was closely related to gross primary production (GPP) according to a number of studies from different regions and satellites (Li et al., 2018; Sun et al., 2017). GPP based phenology as a key indicator of vegetation actual photosynthesis activity was treated as real carbon phenology to verify the accuracy of remotely sensed phenology (Lu et al., 2018a; Wang et al., 2019b; Wu et al., 2013). The comparison of land surface phenology obtained from SIF and GPP indicated that SIF can track the seasonal dynamics of vegetation development well for forests (Jeong et al., 2017).