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

Abstract

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 (R2 = 0.67, RMSE = 2.24g C·m–2·d–1); however, NIRv combined with CO2, plant traits, and climatic factors can achieve the highest prediction accuracy (R2 = 0.87, RMSE = 1.40g C·m–2·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.

Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland

“…One is to conduct large-scale (such as global, continental, and national) GPP prediction research, and the other is to conduct fine-scale (such as pasture, and farm) GPP prediction research. Large-scale GPP predicting research can better promote global sustainable development goals, achieve global carbon balance, and contribute to the monitoring and management of the global ecological environment [92][93][94]. Fine-scale GPP predicting is closer to actual production and can guide grazing, planting, and other industries [95].…”