Abstract
Accurately quantifying gross primary production (GPP) is of vital importance to understanding the global carbon cycle. Light-use efficiency (LUE) models and process-based models have been widely used to estimate GPP at different spatial and temporal scales. However, large uncertainties remain in quantifying GPP, especially for croplands. Recently, remote measurements of solar-induced chlorophyll fluorescence (SIF) have provided a new perspective to assess actual levels of plant photosynthesis. In the presented study, we evaluated the performance of three approaches, including the LUE-based multi-source data synergized quantitative (MuSyQ) GPP algorithm, the process-based boreal ecosystem productivity simulator (BEPS) model, and the SIF-based statistical model, in estimating the diurnal courses of GPP at a maize site in Zhangye, China. A field campaign was conducted to acquire synchronous far-red SIF (SIF760) observations and flux tower-based GPP measurements. Our results showed that both SIF760 and GPP were linearly correlated with APAR, and the SIF760-GPP relationship was adequately characterized using a linear function. The evaluation of the modeled GPP against the GPP measured from the tower demonstrated that all three approaches provided reasonable estimates, with R2 values of 0.702, 0.867, and 0.667 and RMSE values of 0.247, 0.153, and 0.236 mg m−2 s−1 for the MuSyQ-GPP, BEPS and SIF models, respectively. This study indicated that the BEPS model simulated the GPP best due to its efficiency in describing the underlying physiological processes of sunlit and shaded leaves. The MuSyQ-GPP model was limited by its simplification of some critical ecological processes and its weakness in characterizing the contribution of shaded leaves. The SIF760-based model demonstrated a relatively limited accuracy but showed its potential in modeling GPP without dependency on climate inputs in short-term studies.
Highlights
As the underlying process for plant growth, photosynthesis serves as an essential indicator of plant efficiency and agricultural management practices
Over the past several decades, a variety of approaches have been developed to estimate the gross primary production (GPP) of terrestrial ecosystems, and they can be grouped into four categories [7]: (1) light use efficiency (LUE) models; (2) process-based models; (3) data-driven models upscaled from eddy covariance (EC) data; and (4) models based on sun-induced chlorophyll fluorescence (SIF)
We evaluated the ability of three different approaches, SIF, LUE-based multi-source data synergized quantitative (MuSyQ)-GPP, and process-based boreal ecosystem productivity simulator (BEPS), to estimate the diurnal courses of GPP at a maize site
Summary
As the underlying process for plant growth, photosynthesis serves as an essential indicator of plant efficiency and agricultural management practices. By employing EC-derived GPP data, data-driven models use various algorithms, such as model tree ensembles (MTE) [20] or regression tree [21,22] approaches, and provide a practical way to upscale GPP from individual sites to regional scales. These data-driven methods establish a series of rules using data mining that relate in situ flux observations to satellite-based indices and climate data.
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