Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO 2 fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO 2 fertilization effect 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 overlapping 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 programs 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 due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the “Greening Earth”, accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models.

Vegetation controls the exchange of carbon, water, momentum and energy between the land and the atmosphere, and provides food, fibre, fuel and other valuable ecosystem services 1,2. Changes in vegetation structure and function are driven by climatic and environmental changes, and by human activities such as land-use change. Given that increased carbon storage in vegetation, such as through afforestation, could combat climate change 3,4 , quantifying vegetation change and its impact on carbon storage and climate has elicited considerable interest from scientists and policymakers. However, it is not possible to detect vegetation changes at the global scale using ground-based observations due to the heterogeneity of change and the lack of observations that can detect these changes both spatially and temporally. While monitoring the changes in some vegetation properties (for example, stem-size distribution and below-ground biomass) at the global scale remains impossible, satellite-based remote sensing has enabled continuous estimation of a few important metrics, including vegetation greenness, since the 1980s (Box 1). In 1986, a pioneering study by Tucker et al. 5 on remotely sensed normalized difference vegetation index (NDVI; a radiometric measure of vegetation greenness) (Box 1) revealed a close connection between vegetation canopy greenness and photosynthesis acti vity (as inferred from seasonal variations in atmospheric CO 2 concentration). This index was successfully used to constrain vegetation primary production globally 6. Using NDVI data from 1981 to 1991, Myneni et al. 7 reported an increasing trend in vegetation greenness in the Northern Hemisphere, which was subsequently observed across the globe 8-13. This 'vegetation greening' is defined as a statistically signi ficant increase in annual or seasonal vegetation greenness at a location resulting, for instance, from increases in average leaf size, leaf number per plant, plant density, species composition, duration of green-leaf presence due to changes in the growing season and increases in the number of crops grown per year. There has also been considerable interest in understanding the mechanisms or drivers of greening 11,14. Lucht et al. 14 and Xu et al. 10 revealed that warming has eased climatic constraints, facilitating increasing vegetation greenness over the high latitudes. Zhu et al. 11 further investigated key drivers of greenness trends and concluded that CO 2 fertilization is a major factor driving vegetation greening at the global scale. Subsequent studies based on fine-resolution and medium-resolution satel lite data 13 have shown the critical role of land-surface history, including afforestation and agricultural intensification, in enhancing vegetation greenness. The large spatial scale of vegetation greening and the robustness of its signal have led the Intergovernmental Panel on Climate Change (IPCC) special report on climate change Afforestation The conversion of treeless lands to forests through planting trees.

Monitoring and understanding climate-induced changes in the boreal and arctic vegetation is critical to aid in prognosticating their future. We used a 33 year long record of satellite observations to robustly assess changes in metrics of growing season (onset: SOS, end: EOS and length: LOS) and seasonal total gross primary productivity. Particular attention was paid to evaluating the accuracy of these metrics by comparing them to multiple independent direct and indirect growing season and productivity measures. These comparisons reveal that the derived metrics capture the spatio-temporal variations and trends with acceptable significance level (generally p<0.05). We find that LOS has lengthened by 2.60 d dec −1 (p<0.05) due to an earlier onset of SOS (−1.61 d dec −1 , p<0.05) and a delayed EOS (0.67 d dec −1 , p<0.1) at the circumpolar scale over the past three decades. Relatively greater rates of changes in growing season were observed in Eurasia (EA) and in boreal regions than in North America (NA) and the arctic regions. However, this tendency of earlier SOS and delayed EOS was prominent only during the earlier part of the data record (1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999). During the later part (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014), this tendency was reversed, i.e. delayed SOS and earlier EOS. As for seasonal total productivity, we find that 42.0% of northern vegetation shows a statistically significant (p<0.1) greening trend over the last three decades. This greening translates to a 20.9% gain in productivity since 1982. In contrast, only 2.5% of northern vegetation shows browning, or a 1.2% loss of productivity. These trends in productivity were continuous through the period of record, unlike changes in growing season metrics. Similarly, we find relatively greater increasing rates of productivity in EA and in arctic regions than in NA and the boreal regions. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation during last three decades.

Abstract:The aim of this paper is to assess the latest version of the MODIS LAI/FPAR product (MOD15A2H), namely Collection 6 (C6). We comprehensively evaluate this product through three approaches: validation with field measurements, intercomparison with other LAI/FPAR products and comparison with climate variables. Comparisons between ground measurements and C6, as well as C5 LAI/FPAR indicate: (1) MODIS LAI is closer to true LAI than effective LAI; (2) the C6 product is considerably better than C5 with RMSE decreasing from 0.80 down to 0.66; (3) both C5 and C6 products overestimate FPAR over sparsely-vegetated areas. Intercomparisons with three existing global LAI/FPAR products (GLASS, CYCLOPES and GEOV1) are carried out at site, continental and global scales. MODIS and GLASS (CYCLOPES and GEOV1) agree better with each other. This is expected because the surface reflectances, from which these products were derived, were obtained from the same instrument. Considering all biome types, the RMSE of LAI (FPAR) derived from any two products ranges between 0.36 (0.05) and 0.56 (0.09). Temporal comparisons over seven sites for the [2001][2002][2003][2004] period indicate that all products properly capture the seasonality in different biomes, except evergreen broadleaf forests, where infrequent observations due to cloud contamination induce unrealistic variations. Thirteen years of C6 LAI, temperature and precipitation time series data are used to assess the degree of correspondence between their variations. The statistically-significant associations between C6 LAI and climate variables indicate that C6 LAI has the potential to provide reliable biophysical information about the land surface when diagnosing climate-driven vegetation responses.