Abstract Vegetation greening trends are a critical and direct indicator to reflect photosynthetic activity of plants at the ecosystem scale. The monitoring of vegetation greening is crucial for assessing ecosystem health, sustaining biodiversity, improving soil health, and providing direction for effective environmental management. However, long-term changes in greening trends are rarely reported due to radiometric inconsistencies among different satellite sensors. Here, we used 12 machine learning (ML) algorithms to perform pixel correction on 42 years of MODIS normalized difference vegetation index (NDVI) and GIMMS NDVI data. The models exhibited high accuracy (93%–97%), yielding a robust ensemble R2 of 0.88 at the spatial scale. From 1982 to 2023, global NDVI had an increasing trend (0.0012), particularly in the high-latitude regions of the Northern Hemisphere (> 60°N), with the rate of increase accelerating since 2000. Temperature and water conditions showed significant correlations with greening trends, exhibiting spatially asymmetric effects. In addition, frequent land cover changes (FLCC) promoted the growth of greening trends (~23%), which is linked to enhanced structural plasticity of vegetation. This effect has become more pronounced since 2000, with 92% of such changes occurring during this period. Anthropogenic impact may play a role in driving greening trend reductions, rather than enhancements.
Bao et al. (Fri,) studied this question.
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