Chlorophyll-Based Canopy Photosynthesis Model: Development and Global Synergy Analysis

Abstract

Accurate estimation of gross primary production (GPP) is essential for understanding the carbon cycle in terrestrial ecosystems. Although numerous models have been developed to estimate GPP, many require a large number of input variables and parameters. For example, light-use efficiency (LUE) models typically rely on multiple inputs, such as photosynthetically active radiation (PAR), the fraction of absorbed PAR, air temperature, vapor pressure deficit, and biome-specific parameters. This dependence on multiple data sources can introduce additional uncertainties into GPP estimates. Here, we developed and evaluated an efficient chlorophyll-based canopy photosynthesis model (CPM) for estimating GPP using the Sentinel-3 OLCI-derived canopy chlorophyll content (CCC) and the canopy chlorophyll absorption coefficient in the red-edge band ({$\alpha$}RE). In the CPM framework, GPP is estimated from the product of potential incident PAR (PARpot) and remotely sensed indicators of photosynthetic capacity, such as CCC or {$\alpha$}RE. By using PARpot instead of actual PAR, CPM reduces its dependence on ancillary environmental inputs. We evaluated the CCC-based and {$\alpha$}RE-based CPM models using data from 301 eddy covariance flux sites distributed globally. Based on leave-one-site-out cross-validation, the global CPM models accurately estimated GPP using CCC~\texttimes PARpot (R2=0.76, RMSE=1.79 gC{$\cdot$}m-2{$\cdot$}d-1, nRMSE=6.73%) and {$\alpha$}RE\texttimes PARpot (R2=0.73, RMSE=1.91 gC{$\cdot$}m-2{$\cdot$}d-1, nRMSE=7.15%). The CPM models outperformed the widely used near-infrared reflectance of vegetation (NIRv)-based method and the MODIS GPP product. We further evaluated the ability of CCC\texttimes PARpot to capture global photosynthetic patterns using TROPOMI sun-induced chlorophyll fluorescence (SIF) as a benchmark and found a strong correlation between CCC\texttimes PARpot and SIF (R2{$>~$}0.80). These findings demonstrate that the CPM framework relying solely on CCC and {$\alpha$}RE provides accurate and consistent estimates of GPP, offering a practical approach for improving assessments of the global carbon cycle and ecosystem responses to climate change.

Dr. Dong Li

Research Scientist

My research interests include vegetation remote sensing and precision agriculture.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.