FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites 

Abstract. Accurately estimating Gross Primary Productivity (GPP) in terrestrial ecosystems is essential for understanding the global carbon cycle. Satellite-based Light Use Efficiency (LUE) models are commonly employed for simulating GPP. However, the variables and algorithms related to environmental limiting factors differ significantly across various LUE models. In this work, we developed a series of FLAML-LUE models tailored for different ecosystems. These models utilize the Fast Lightweight Automated Machine Learning (FLAML) framework, using variables of LUE models, to investigate the potential of estimating site-scale GPP. Incorporating meteorological data, eddy covariance measurements, and remote sensing indices, we employed FLAML-LUE models to assess the impact of various variable combinations on GPP across different temporal scales, including daily, 8-day, 16-day, and monthly intervals. Cross-validation analyses indicated that the effectiveness of FLAML-LUE models for forest ecosystems varied significantly across different sites, with R² values ranging from 0.56 to 0.94. For grassland ecosystems, R² values ranged from 0.62 to 0.87, and for cropland ecosystems, R² values ranged from 0.78 to 0.88. Extending the time scale of input data could significantly enhance the accuracy of model simulations. Specifically, the average R² increased from 0.82 to 0.92 for forest ecosystems, 0.79 to 0.83 for grassland ecosystems, and 0.84 to 0.87 for farmland ecosystems. Additionally, the importance ranking method indicated that vegetation index and temperature were the most important variables for GPP estimation in forest, grassland, and farmland ecosystems, while the importance of the moisture index was relatively low. This study offers an approach to estimate GPP fluxes and evaluate the impact of variables on GPP estimation. It has the potential to be applied in predicting GPP for different vegetation types at a regional scale.