Effects of the Temporal Aggregation and Meteorological Conditions on the Parameter Robustness of OCO-2 SIF-Based and LUE-Based GPP Models for Croplands

Abstract

Global retrieval of solar-induced chlorophyll fluorescence (SIF) using remote sensing by means of satellites has been developed rapidly in recent years. Exploring how SIF could improve the characterization of photosynthesis and its role in the land surface carbon cycle has gradually become a very important and active area. However, compared with other gross primary production (GPP) models, the robustness of the parameterization of the SIF model under different circumstances has rarely been investigated. In this study, we examined and compared the effects of temporal aggregation and meteorological conditions on the stability of model parameters for the SIF model ( ε / S I F yield ), the one-leaf light-use efficiency (SL-LUE) model ( ε max ), and the two-leaf LUE (TL-LUE) model ( ε msu and ε msh ). The three models were parameterized based on a maize–wheat rotation eddy-covariance flux tower data in Yucheng, Shandong Province, China by using the Metropolis–Hasting algorithm. The results showed that the values of the ε / S I F yield and ε max were similarly robust and considerably more stable than ε msu and ε msh for all temporal aggregation levels. Under different meteorological conditions, all the parameters showed a certain degree of fluctuation and were most affected at the mid-day scale, followed by the monthly scale and finally at the daily scale. Nonetheless, the averaged coefficient of variation ( C V ) of ε / S I F yield was relatively small (15.0%) and was obviously lower than ε max ( C V = 27.0%), ε msu ( C V = 43.2%), and ε msh ( C V = 53.1%). Furthermore, the SIF model’s performance for estimating GPP was better than that of the SL-LUE model and was comparable to that of the TL-LUE model. This study indicates that, compared with the LUE-based models, the SIF-based model without climate-dependence is a good predictor of GPP and its parameter is more likely to converge for different temporal aggregation levels and under varying environmental restrictions in croplands. We suggest that more flux tower data should be used for further validation of parameter convergence in other vegetation types.

On the Covariation of Chlorophyll Fluorescence and Photosynthesis Across Scales

“…The more sunlit leaves in the field of view, the higher the escape fraction ( f esc, Yang & van der Tol, 2018; Zeng et al, 2019), and thus higher SIF at an equivalent APAR. Temporally, integrating GPP and SIF over days or months results in a more linear SIF‐GPP relationship (Lin et al, 2019; Magney, Bowling, et al, 2019; Yang et al, 2015). This is primarily due to the idea that variability in photosynthetic efficiency decreases with increasing temporal scale (Medlyn, 1998).…”

Global‐Scale Consistency of Spaceborne Vegetation Indices, Chlorophyll Fluorescence, and Photosynthesis

“…Recent scientific advancements enable us to retrieve solar‐induced chlorophyll fluorescence (SIF) from spaceborne spectroscopic measurements (Frankenberg et al., 2011; Guanter et al., 2007; Joiner, Yoshida, Vasilkov, & Middleton, 2011), which is light energy emitted by plants after chlorophyll absorbs sunlight. Spaceborne SIF data has been shown to be linearly correlated with GPP at coarse spatial and temporal scales (Lin et al., 2019; Magney, Barnes, & Yang, 2020; Magney, Bowling, et al., 2019; X. Yang et al., 2015). Thus, spaceborne SIF has become an accepted proxy and is a new tool for assessing the performance of GPP products (Sun et al., 2017; Wagle, Zhang, Jin, & Xiao, 2016; Y. Zhang et al., 2016).…”

The Links between Canopy Solar-Induced Chlorophyll Fluorescence and Gross Primary Production Responses to Meteorological Factors in the Growing Season in Deciduous Broadleaf Forest

“…The coupling mechanism process of SIF and GPP on a seasonal scale also needs further study since the accuracy of GPP estimates by SIF is influenced by the multiple meteorological factors [36]. Airborne platforms and satellite remote sensing obtain instantaneous data of SIF on discrete days [34,37].…”