Estimating Forest Gross Primary Production Using Machine Learning, Light Use Efficiency Model, and Global Eddy Covariance Data

Forests play a vital role in atmospheric CO2 sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R2 and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%.