Exploring the Best-Matching Plant Traits and Environmental Factors for Vegetation Indices in Estimates of Global Gross Primary Productivity

As the largest source of uncertainty in carbon cycle studies, accurate quantification of gross primary productivity (GPP) is critical for the global carbon budget in the context of global climate change. Numerous vegetation indices (VIs) based on satellite data have participated in the construction of GPP models. However, the relative performance of various VIs in predicting GPP and what additional factors should be combined with them to reveal the photosynthetic capacity of vegetation mechanistically better are still poorly understood. We constructed two types of models (universal and plant functional type [PFT]-specific) for solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and Leaf Area Index (LAI) based on two widely used machine learning algorithms, i.e., the random forest (RF) and back propagation neural network (BPNN) algorithms. A total of thirty plant traits and environmental factors with legacy effects are considered in the model. We then systematically investigated the ancillary variables that best match each vegetation index in estimating global GPP. Four types of models (universal and PFT-specific, RF and BPNN) consistently show that SIF performs best when modeled using a single vegetation index (R-2 = 0.67, RMSE = 2.24 g C center dot m(-2)center dot d(-1)); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R-2 = 0.87, RMSE = 1.40 g C center dot m(-2)center dot d(-1)). Plant traits effectively enhance all prediction models' accuracy, and climatic variables are essential factors in improving the accuracy of NIRv- or LAI-based GPP models, but not the accuracy of SIF-based models. Our findings provide valuable information for the configuration of the data-driven models to improve the accuracy of predicting GPP and provide insights into the physiological and ecological mechanisms underpinning GPP prediction.

Automated summary

This study explores the relative performance of different vegetation indices in predicting gross primary productivity (GPP) and investigates additional factors that can better reveal the photosynthetic capacity of vegetation. The results show that solar-induced chlorophyll fluorescence (SIF) performs best when modeled using a single vegetation index, while NIRv combined with CO2, plant traits, and climatic factors achieves the highest prediction accuracy.