Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon
flux, and its accurate simulation and future prediction play an important role in …

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving
as a sensitive indicator of ecosystems' responses to climate change. However, the impact of …

Advancements in remote sensing technology have significantly contributed to the
improvement of models for estimating terrestrial gross primary productivity (GPP). However …

Photosynthesis (a key ecological process) is measured based on gross primary productivity
(GPP), emphasizing the criticality of accurate GPP estimation to climate change research …

Upscaling flux tower measurements based on machine learning (ML) algorithms is an
essential approach for large‐scale net ecosystem CO2 exchange (NEE) estimation, but …

The quantification of carbon fluxes (CFs) is crucial due to their role in the global carbon cycle
having a direct impact on Earth's climate. In the last years, considerable efforts have been …

Introduction: Machine learning methods combined with satellite imagery have the potential
to improve estimates of carbon uptake of terrestrial ecosystems, including croplands …

Gross primary production (GPP) measures the amount of carbon fixed by plants and, thus,
plays a significant role in the terrestrial carbon cycle and global food security, especially in …