Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services 1,2 . Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982-2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAI (browning). Factorial simulations with multiple global ecosystem models suggest that CO 2 fertilization e ects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO 2 fertilization e ects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. The regional e ects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, di erences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.Changes in vegetation greenness have been reported at regional and continental scales on the basis of forest inventory and satellite measurements 3-8 . Long-term changes in vegetation greenness are driven by multiple interacting biogeochemical drivers and land-use effects 9 . Biogeochemical drivers include the fertilization effects of elevated atmospheric CO 2 concentration (eCO 2 ), regional climate change (temperature, precipitation and radiation), and varying rates of nitrogen deposition. Land-use-related drivers involve changes in land cover and in land management intensity, including fertilization, irrigation, forestry and grazing 10 . None of these driving factors can be considered in isolation, given their strong interactions with one another. Previously, a few studies had investigated the drivers of global greenness trends 6,7,11 , with a limited number of models and satellite observations, which prevented an appropriate quantification of uncertainties 12 .Here, we investigate trends of leaf area index (LAI) and their drivers for the period 1982 to 2009 using three remotely sensed data sets (GIMMS3g, GLASS and GLOMAP) and outputs from ten ecosystem models run at global extent (see Supplementary Information). We use the growing season integrated leaf area index (hereafter, LAI; Methods) as the variable of our study. We first analyse global and regional LAI trends for the study period and differences between the three data sets. Using modelling results, we then quantify the contributions of CO 2 fertilization, climatic factors, nitrogen deposition and LCC to the observed trends...

Long-term global data sets of vegetation Leaf Area Index (LAI) [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009]. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.

Maximum light use efficiency (ε max ) is a key parameter for the estimation of net primary productivity (NPP) derived from remote sensing data. There are still many divergences about its value for each vegetation type. The ε max for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The vegetation classification accuracy is introduced to the process. The sensitivity analysis of ε max to vegetation classification accuracy is also conducted.The results show that the simulated values of ε max are greater than the value used in CASA model, and less than the values simulated with BIOME-BGC model. This is consistent with some other studies. The relative error of ε max resulting from classification accuracy is −5.5%-8.0%. This indicates that the simulated values of ε max are reliable and stable.

Aim We intend to characterize and understand the spatial and temporal patterns of vegetation phenology shifts in North America during the period 1982-2006. Location North America.Methods A piecewise logistic model is used to extract phenological metrics from a time-series data set of the normalized difference vegetation index (NDVI). An extensive comparison between satellite-derived phenological metrics and groundbased phenology observations for 14,179 records of 73 plant species at 802 sites across North America is made to evaluate the information about phenology shifts obtained in this study. ResultsThe spatial pattern of vegetation phenology shows a strong dependence on latitude but a substantial variation along the longitudinal gradient. A delayed dormancy onset date (0.551 days year -1 , P = 0.013) and an extended growing season length (0.683 days year -1 , P = 0.011) are found over the mid and high latitudes in North America during 1982-2006, while no significant trends in greenup onset are observed. The delayed dormancy onset date and extended growing season length are mainly found in the shrubland biome. An extensive validation indicates a strong robustness of the satellite-derived phenology information.Main conclusions It is the delayed dormancy onset date, rather than an advanced greenup onset date, that has contributed to the prolonged length of the growing season over the mid and high latitudes in North America during recent decades. Shrublands contribute the most to the delayed dormancy onset date and the extended growing season length. This shift of vegetation phenology implies that vegetation activity in North America has been altered by climatic change, which may further affect ecosystem structure and function in the continent.

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Since current administrative unit-based urban land area statistical data in China lack enough spatial information, the urbanization process research at large scale cannot be effectively supported. Based on the current administrative unit-based urban land area statistical data in China, a new approach to quickly and cheaply derive urban land information from the non-radiance-calibrated Defense Meteorological Satellite Program/ Operational Linescan System (DMSP/OLS) nighttime light imagery is presented in this paper. With the new approach, the urban pattern information in China in 1992, 1996 and 1998 was derived with the urbanization processes in China in the 1990s restored by using the non-radiance-calibrated DMSP/OLS nighttime imagery. The accuracy assessment based on the statistical data showed that the relative error between the derived total urban land area and the statistical data at national scale was less than 2% in 1992, and less than 1% in 1996 and 1998, and the maximum relative error at province scale do not exceed 10% with most of the provinces less than 3%. In addition, the urban patterns derived from the high-resolution Landsat TM imagery were compared with those from the DMSP/ OLS data. The results showed that the urban pattern characteristics derived from DMSP/OLS were basically coincident with those from TM imagery with the total accuracy of about 80%. Thus it can be seen that our restored urbanization process in China in the 1990s by using the non-radiance DMSP/OLS nighttime imagery can be accepted and can represent the actual urban development in China at that time on the whole.

In this paper, we compared the concept of agricultural drought and its relationship with other types of droughts and reviewed the progress of research on agricultural drought monitoring indices on the basis of station data and remote sensing. Applicability and limitations of different drought monitoring indices were also compared. Meanwhile, development history and the latest progress in agricultural drought monitoring were evaluated through statistics and document comparison, suggesting a transformation in agricultural drought monitoring from traditional single meteorological monitoring indices to meteorology and remote sensing-integrated monitoring indices. Finally, an analysis of current challenges in agricultural drought monitoring revealed future research prospects for agricultural drought monitoring, such as investigating the mechanism underlying agricultural drought, identifying factors that influence agricultural drought, developing multi-spatiotemporal scales models for agricultural drought monitoring, coupling qualitative and quantitative agricultural drought evaluation models, and improving the application levels of remote sensing data in agricultural drought monitoring.

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