Evaluating Cumulative Drought Effect on Global Vegetation Photosynthesis Using Numerous GPP Products

The increasing trend in drought events under the background of global warming makes it more important to understand effect on vegetation photosynthesis. While diverse gross primary production (GPP) datasets were adopted investigate impact photosynthesis, few studies focused discrepancies response among different GPP datasets, especially for cumulative impact. Therefore, a total twenty-six based process, machine learning (ML), and light-use efficiency (LUE) model schemes obtained appraise stress photosynthesis from 2001 2010. Moreover, relatively reliable pattern drought’s was acquired these products through probability analysis. results illustrated that existed 52.11% cover land with time scales dominantly at short term (1–4 months, 31.81%). Obvious heterogeneity space functional types observed, as reliability decreased latitude decreasing higher sensitivity herbaceous than woody plants. Our findings highlighted importance ways characterizing moisture conditions across various models necessity dataset selection investigating