China and India lead in greening of the world through land-use management
By Chi Chen, T. Park, Xuhui Wang, Shilong Piao, Baodong Xu, Rajiv K. Chaturvedi, R. Fuchs, V. Brovkin, Philippe Ciais, R. Fensholt and 5 more. Nature Sustainability, 2019
2,471 citations recorded by the source corpus.
Satellite data show increasing leaf area of vegetation due to direct factors (human land-use management) and indirect factors (such as climate change, CO2 fertilization, nitrogen deposition and recovery from natural disturbances). Among these, climate change and CO2 fertilization effects seem to be the dominant drivers. However, recent satellite data (2000–2017) reveal a greening pattern that is strikingly prominent in China and India and overlaps with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programmes to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly owing to an increase in harvested area through multiple cropping facilitated by fertilizer use and surface- and/or groundwater irrigation. Our results indicate that the direct factor is a key driver of the ‘Greening Earth’, accounting for over a third, and probably more, of the observed net increase in green leaf area. They highlight the need for a realistic representation of human land-use practices in Earth system models.Since the early 1980s, remotely sensed data has shown the Earth to be slowly greening. Climate change, CO2 fertilization and land-use change are competing explanations. Using satellite data from 2000–2017, this study finds striking greening of both China and India, driven primarily by land-use change, with forest growth and cropland intensification more important in China and cropland more important in India.
Subfields: Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/2055c468a3bc4ab348c8d239fce91edb7d27aae3
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In Ecology
In Global and Planetary Change
- Pierre Gentine
- Jean-François Soussana
- Pete Smith
- Chunyang He
- Vincent Humphrey
- Miaogen Shen
- Claire Chenu
- Sonia I. Seneviratne
Citations in the corpus
- Botanic garden solutions to the plant extinction crisis (2020, background): “In fact, the effect can be the opposite, as already seen in many tree plantations in both tropical and temperate regions (Chen et al., 2019; Zhai, Xu, Dai, Cannon, & Grumbine, 2013).”
- The occupation of cropland by global urban expansion from 1992 to 2016 and its implications (2020, methodology): “Considering the trend of ‘global greening’ by CO2 enhancement (Chen et al 2019), we used the long-term average values of MODIS NPP as a proxy to estimate the impacts of urban expansion on cropland NPP.”
- How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal (2019, methodology): “For example, earth observation can be used to estimate changes in carbon input to soils, through changes in NPP/GPP (Chen et al., 2019; Neumann & Smith, 2018), land degrada‐ tion (Sims et al., 2019) and can also be used to determine land cover/ land cover change (e.g. Chen et al., 2019).”
- Coupling between the terrestrial carbon and water cycles—a review (2019, background): “…(e.g. water consumption for agriculture, industry) still remains an issue (Ho et al 2016, 2017), even if it is improving in many regions of the globe and if remote sensing observations have highlighted that they are the main contributors to overall global greening of the planet (Chen et al 2019).”
- Factors Affecting Long-Term Trends in Global NDVI (2019, methodology): “Secondly, some studies used LAI [6,16,18,58], Fracti nal Vegetation Cov r (FVC) [59], Enhanced Vegetation Index (EVI) [60], and even growth season data [16] to characterize vegetation.”