Machine learning-based modeling of vegetation leaf area index and gross primary productivity across North America and comparison with a process-based model

Abstract

Vegetation plays a key role in regulating the material and energy exchanges among the biosphere, the atmosphere, and the pedosphere. Modeling and predicting vegetation key variables such as leaf area index (LAI) and gross primary productivity (GPP) is crucial to understand and project the processes of vegetation growth in response to climate change. While a number of studies developed models to simulate vegetation GPP using satellite-derived LAI, the requirement of satellite-based model inputs largely limits the predicting power of these developed models. This study developed the machine learning models, including both support vector regression (SVR) and random forests (RF), which are capable of modeling LAI and GPP time series using only meteorological variables. We first simulated the LAI time series directly using meteorological variables as inputs to the machine learning models and then buffered its unrealistic day-to-day fluctuation, and further modeled the GPP time series using meteorological variables and modeled LAI time series. We tested our methods for four main plant functional types across North America and evaluated the models using both satellite-based and flux tower data. The results demonstrate that the machine learning models perform well on simulating the time series of both LAI and GPP. We identified that there is a need to improve the phenology representation in the Biome-BGC model. The machine learning models provide an alternative way to predict time series of LAI and GPP using only meteorological variables across large geographic regions, and also provide benchmarking accuracies for future developments of the process-based models.