Satellite-Based Estimation of Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau: A Multi-Model Comparison

Abstract: Alpine swamp meadows on the Tibetan Plateau, with the highest soil organic carbon content across the globe, are extremely vulnerable to climate change. To accurately and continually quantify the gross primary production (GPP) is critical for understanding the dynamics of carbon cycles from site-scale to global scale. Eddy covariance technique (EC) provides the best approach to measure the site-specific carbon flux, while satellite-based models can estimate GPP from local, small scale sites to regional and global scales. However, the suitability of most satellite-based models for alpine swamp meadow is unknown. Here we tested the performance of four widely-used models, the MOD17 algorithm (MOD), the vegetation photosynthesis model (VPM), the photosynthetic capacity model (PCM), and the alpine vegetation model (AVM), in providing GPP estimations for a typical alpine swamp meadow as compared to the GPP estimations provided by EC-derived GPP. Our results indicated that all these models provided good...