Progress of studies on satellite-based terrestrial vegetation production models in China

Vegetation production is an important variable in terrestrial ecosystems, playing crucial roles in sustaining carbon balance, reducing atmospheric CO2 concentration, and mitigating global climate change. Satellite-based models, which benefit from spatially and temporally continuous remote sensing observations of vegetation growth conditions, are widely used for quantifying regional and global vegetation production. Satellite-based vegetation production models were initially simple statistical models, but later, process-based light use efficiency models were developed. The latest models are based on the relationship between solar-induced chlorophyll fluorescence and vegetation production. An increasing number of satellite-based studies are being conducted by Chinese scientists, who are developing and implementing plant production models, particularly by self-developing a number of light consumption efficiency models and establishing a long-term worldwide dataset of vegetation production. Furthermore, Chinese scientists have investigated the spatial and temporal patterns of vegetation using diverse models, significantly improving our understanding of terrestrial functions and structures. However, current models and estimation techniques need further improvement, and Chinese scientists had the opportunity to improve model capability and our understanding of vegetation production patterns and their regulating elements.

Automated summary

Vegetation production is a crucial variable in terrestrial ecosystems, with significant implications for carbon balance, atmospheric CO2 concentration, and global climate change. Satellite-based models have been widely adopted to quantify regional and global vegetation production, benefiting from continuous remote sensing observations. Chinese scientists have made significant contributions in this field, developing and implementing plant production models, improving our understanding of terrestrial functions and structures. However, there is still room for improvement in current models and estimation techniques.