Global Greening Major Contributed by Climate Change With More Than Two Times Rate Against the History Period During the 21th Century.

ABSTRACT: Future variations of global vegetation are of paramount importance for the socio-ecological systems. However, up to now, it is still difficult to develop an approach to project the global vegetation considering the spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid-by-grid; multi-algorithms; optimal combination) to construct an optimal model using six algorithms (i.e., LR: linear regression; SVR: support vector regression; RF: random forest; CNN: convolutional neural network; and LSTM: long short-term memory; transformer) based on five climatic factors (i.e., Tmp: temperature; Pre: precipitation; ET: evapotranspiration, SM: soil moisture, and CO₂). The optimal model is employed to

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11897688 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature