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ARTICLE IN PRESS Global Environmental Change 16 (2006) 340–348 www.elsevier.com/locate/gloenvcha NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China Shilong Piaoa, Anwar Mohammata, Jingyun Fanga,, Qiang Caia,b, Jianmeng Fenga a Department of Ecology, College of Environmental Sciences, Center for Ecological Research and Education, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China b Department of Geography, University of Iowa, IA 52245, USA Received 11 October 2004; received in revised form 7 February 2006; accepted 27 February 2006 Abstract This study analyzes the temporal change of Normalized Difference Vegetation Index (NDVI) for temperate grasslands in China and its correlation with climatic variables over the period of 1982–1999. Average NDVI of the study area increased at rates of 0.5% yr1 for the growing season (April–October), 0.61% yr1 for spring (April and May), 0.49% yr1 for summer (June–August), and 0.6% yr1 for autumn (September and October) over the study period. The humped-shape pattern between coefficient of correlation (R) of the growing season NDVI to precipitation and growing season precipitation documents various responses of grassland growth to changing precipitation, while the decreased R values of NDVI to temperature with increase of temperature implies that increased temperature declines sensitivity of plant growth to changing temperature. The results also suggest that the NDVI trends induced by climate changes varied between different vegetation types and seasons. r 2006 Elsevier Ltd. All rights reserved. Keywords: China; Climate change; Normalized Difference Vegetation Index (NDVI); Interannual change in NDVI; Spatial pattern of NDVI; Temperate grassland 1. Introduction China’s temperate grassland is the third largest grassland area in the world (Lee et al., 2002). It not only supports the world’s largest population of sheep and goats, but also plays an important role in the regional climate and reducing soil loss by wind or water erosion (Chen and Wang, 2000; Xie et al., 2001). Recent studies (Shi et al., 2003; Qin, 2002) have shown that the climate of this region has experienced dramatic change in the past several decades. This change likely directly affects grassland growth, as primary production of temperate grasslands in China is highly sensitive to interannual variation in climate, especially to the change in precipitation (Xiao et al., 1995; Chen and Wang, 2000). Therefore, studies on the impact of climate change on productivity of temperate grasslands in China are of significance to both the Corresponding author. Tel: +86 10 6276 5578; fax: +86 10 6275 6560. E-mail address: [email protected] (J. Fang). 0959-3780/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.gloenvcha.2006.02.002 understanding of the grassland ecosystem carbon cycle and the sustainable use of grassland resources in China. The relatively simple structure of grassland ecosystems makes them ideal subjects for research using remote sensing imageries to study vegetation dynamics. Normalized Density Vegetation Index (NDVI), which is derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of photosynthesis (vegetation activity). With a broad spatial coverage and high temporal resolution, the advanced very high-resolution radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) NDVI datasets provides unique opportunities for monitoring terrestrial vegetation conditions at regional and global scales (Yang et al., 1997), and has widely been used in research areas of NPP (Potter et al., 1993; Paruelo et al., 1997), vegetation coverage (Tucker et al., 1991; Myneni et al., 1997; Zhou et al., 2001; Los et al., 2001; Piao et al., 2003), biomass (Myneni et al., 2001; Dong et al., 2003), and phenology (Reed et al., 1994; Moulin et al., 1997). ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 Climate change is one of the main drivers of the interannual variation in vegetation activity (Zhou et al., 2001; Schimel et al., 2001). Investigations of the correlation between NDVI and climate factors aid in finding key factors that control changes in the terrestrial ecosystem carbon cycle and shed light on the mechanisms controlling the response of terrestrial carbon storage to climate variability (Braswell et al., 1997; Potter and Brooks, 1998). Since the 1980s, a number of analyses have explored the relationships between NDVI and climate factors in different geographic areas and ecosystems. However, the mechanisms of the response of the vegetation to climate change are far from clear (Wang et al., 2003). Most of these studies have related NDVI with climate factors during the growing season or examined their spatial changes (Schultz and Halpert, 1995; Yang et al., 1997; Potter and Brooks, 1998; Suzuki et al., 2000). Few studies have documented the relationships between change in NDVI and climate variables over time (season), and described their spatial patterns (but see Jobbagy et al., 2002; Zhou et al., 2003; Wang et al., 2003; Piao et al., 2003, 2004). Previous studies have suggested that vegetation activity in most region of China increased over the past two decades (Piao et al., 2003, 2004; Fang et al., 2004), but the linkage between climate change and vegetation growth of temperate grasslands has not been adequately quantified, because these studies focused mainly on vegetation responses to climate change at national scale. Furthermore, no effort has been made to investigate the effects of climate change on the relationships between vegetation growth and climate variables, although this is essential for quantifying and understanding future interactions between climate and terrestrial ecosystems. The main purpose of this study is to explore the interannual variation in growth of China’s temperate grasslands from 1982 to 1999, and to investigate the relationships between NDVI and climate variables using concurrent satellite and climate data sets. More specifically, we are to address the following questions: (1) how has the productivity of temperate grasslands analyzed by NDVI changed over the past two decades in China? (2) how does this change correlate with climate change? and (3) how does the relationship between NDVI and climate vary temporally? In order to avoid spurious NDVI trends due to winter snow, our study focuses on growing season NDVI (Zhou et al., 2001; Piao et al., 2004). Here we define growing season as April–October, and further divide the growing season into three seasons: spring (April and May), summer (June–August), and autumn (September and October). 2. Data and methods 2.1. Study area Temperate grasslands in China are mainly distributed north of 35 1N, with an area of 164 104 km2, comprising nearly 17% of China’s territory. A strong east-to-west precipitation gradient results in a decrease in annual precipitation from more than 600 mm in northeastern China to less than 200 mm in northwestern China. With this large precipitation range, China’s temperate grassland is divided into three major types, temperate meadow, temperate steppe, and temperate desert steppe (Fig. 1). Temperate meadows mainly occur in northeastern China, with annual average precipitation ranging from 300 to 600 mm and annual mean temperature from 2 to 5 1C. Temperate steppe spreads from approximately 1081 to 1201 E in longitude and 401 to 501 N in latitude. The annual precipitation in this area is approximately 200 to 400 mm, mainly falling in July and August, while annual average temperature ranges from 0 to 8 1C. Temperate desert steppe extends to the west of 108 1E in longitude, where annual precipitation is only 0–200 mm and annual mean temperature is between 5 and 10 1C. N 45 70 341 80 90 100 110 120 130140 40 35 30 25 20 15 Temperate steppe Temperate desert Temperate meadow Fig. 1. Spatial distribution of temperate grasslands in China (based on the Commission for Integrated Survey of Natural Resources, 1996). ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 2.2. Data sets 0.30 3. Results and discussion 3.1. Trends and interannual variations in mean growing season NDVI Fig. 2 illustrates the variations in mean growing season NDVI, growing season precipitation, growing season (a) y=0.0014x - 2.586 R=0.73, P=0.001 (b) y=0.7899x - 1310.3 R=0.14, P=0.60 0.28 0.26 0.24 350 250 16 Precipitation (mm) 400 300 Temperature (°C) NDVI data at a spatial resolution of 8 8 km and 15day interval were acquired from the Global Inventory Monitoring and Modeling Studies (GIMMS) group derived from the NOAA/AVHRR Land data set for the period January 1982 to December 1999. The dataset is known for its high quality, having been calibrated to eliminate noise from volcano eruptions, solar angle and sensor errors, and has been widely used in studies on vegetation dynamics at regional and global scales (Myneni et al., 2001; Zhou et al., 2001; Slayback et al., 2003). The maximum NDVI value for each pixel during a given month was assigned as the monthly value in an effort to further reduce residual atmospheric and bidirectional effects. NDVI images of spring (April and May), summer (June–August), fall (September and October), and growing season (April–October) were generated separately through computed averages of respective months. Furthermore, in order to eliminate the impact of bare and sparsely vegetated regions, only grid cells with annual mean NDVI greater than 0.1 during the 18 year period were used in this study. Monthly climate data used in this study include temperature and precipitation data, produced by our research group, for the years 1982–1999 (Piao et al., 2003). These datasets were generated from 680 welldistributed climate stations across China by using kriging interpolation (Piao et al., 2003). In order to explore the relationships between vegetation growth and moisture condition, Thornthwaite’s moisture index (Im), an integrated bioclimatic index that involves precipitation and temperature parameter (Thornthwaite, 1948; Fang and Yoda, 1990; Potter et al., 1993), was calculated in this study. The information on distribution of temperate grasslands was obtained from grassland map of China with a scale of 1:4,000,000 (Commission for Integrated Survey of Natural Resources, Chinese Academy of Sciences, 1996), and vegetation map of China with a scale of 1:1,000,000 (Editorial Board of Vegetation Map of China, 2001). Based on these maps, there are 17 grassland types in China. To easily document different responses of grassland growth to climate change, the grasslands in the temperature region in China (north of 35 1N) were further grouped into three types: temperate desert steppe (temperate steppe-desert and temperate desert), temperate steppe (temperate meadowsteppe, temperate steppe and temperate desert-steppe), and temperate meadow (lowland meadow) (Fig. 1). NDVI 2 200 (c) 15 y=0.0521x - 89.071 R=0.59, P=0.009 14 13 -20 (d) y=0.0256x - 80.877 R=0.04, P=0.883 -25 -30 Im 342 -35 1982 1984 1986 1988 1990 1992 1994 1996 1998 Year -40 Fig. 2. Interannual changes in (a) area-weighted growing season normalized difference vegetation index (NDVI), (b) growing season precipitation (mm), (c) growing season mean temperature (1C), and (d) Thornthwaite’s moisture index (Im) over the period 1982–1999 for temperate grasslands in China. temperature and Im in the study area during 1982–1999. Mean growing season NDVI increased significantly (R ¼ 0.73, P ¼ 0.001) from 0.25 in 1982–0.28 in 1999, with an annual increase of 0.0014 (Fig. 2(a)). While growing season precipitation and Im showed only a weak increase (R ¼ 0.14, P ¼ 0.60 and R ¼ 0.04, P ¼ 0.883, respectively), the patterns of rainfall fluctuation corresponded well to that of NDVI (Fig. 2(b and d)). In 1984, 1988, 1990 and 1998, growing season precipitation and Im was relatively high, coinciding with peaks of growing season NDVI. Similarly, the growing season NDVI minima of 1986, 1989, and 1995 correspond with the minima of the growing season precipitation and Im over these years. Growing season mean temperature dramatically increased over the 18 years, with an average increase of 0.05 1C per year (R ¼ 0.59, P ¼ 0.009) (Fig. 2(c)). NDVI trends for different grassland types allow us to better understand patterns of NDVI change. With an increase of mean growing season NDVI of the study area, mean growing season NDVI for all these three grassland types clearly increased (Po0.05) from 1982 to 1999. Temperate meadow exhibited the most significant ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 increasing trend, with an average annual increment of 0.0016 (R ¼ 0.82, Po0.001) (Fig. 3(a)), and temperate desert steppe the largest annual increase rate, at 0.71% (Fig. 3(c)). For temperate meadow, interannual variation in growth season NDVI corresponded closely to that of temperature (Fig. 3(a)). Peak temperature values were reached in 1985, 1988, 1990, 1994, and 1998, which corresponded to higher NDVI values in the same years (except for in 1998). Lower NDVI values in 1983, 1986, 1989, 1992, and 1995 Temperate steppe 0.35 0.33 P=-0.1808x + 712.28 R=0.02, P=0.928 460 410 310 15 T=0.0467x -79.02 R=0.58, P=0.012 14 13 Im=-0.1157x + 212.44 R=-0.12, P=0.634 0 NDVI=0.0015x - 2.7815 R=0.66, P=0.003 0.28 0.26 P=0.9467x -1589.9 R=0.13, P=0.615 410 360 310 260 T=0.052x -89.294 R=0.584, P=0.011 15 210 14 Im=0.0311x -89.02 R=0.04, P=0.884 13 -10 -10 -20 Im Im Temperature (°C) 360 0.30 -30 -20 (a) 1982 1984 1986 1988 1990 1992 1994 1996 1998 Year Precipitation (mm) NDVI 0.37 0.32 NDVI=0.0016x -2.8215 R=0.82,P<0.001 Precipitation (mm) NDVI Temperate meadow accordingly corresponded with lower temperatures. For temperate steppe and temperate desert steppe, NDVI, precipitation and Im show similar pattern of interannual variability (Fig. 3(b and c)). For example, precipitation, Im and NDVI of temperate steppe peaked in 1988, 1990, 1996, and 1998, while were relatively low in 1986, 1989, 1991, and 1997. Such an increase in NDVI derived primarily from climate changes suggests that the vegetation productivity of temperate grasslands, to some extent, may overcome Temperature (°C) 0.39 343 -30 (b) 1982 1984 1986 1988 1990 1992 1994 1996 1998 Year -40 0.17 Temperate desert steppe NDVI=0.0011x - 1.9894 R=0.62, P=0.006 0.15 0.13 P=1.2116x -2288.5 R=0.31, P=0.207 170 140 Temperature (˚C) 110 17 T=0.0564x -96.395 R=0.53, P=0.024 80 Im=0.1231x -290.84 R=0.28, P=0.255 -40 Precipitation (mm) NDVI 0.19 16 15 14 Im -45 -50 (c) 1982 1984 1986 1988 1990 1992 1994 1996 1998 Year -55 Fig. 3. Interannual changes in area-weighted growing season NDVI, growing season precipitation (mm), growing season mean temperature (1C) and Thornthwaite’s moisture index (Im) for (a) temperature meadow, (b) temperature steppe, and (c) temperature desert steppe during 1982–1999. ARTICLE IN PRESS negative effects of overgrazing in the study period. This also can be evidenced by ground-based observations. Several studies have revealed that desertification in some parts of north China has been reversed since the 1980s (Runnström, 2000; Zhong and Qu, 2003; Fang et al., 2004). The frequency of sand storms in north China has decreased by about ten days in the 1990s compared to the 1980s (Qian et al., 2002). The reduction in sandstorms and possible, localized reversal of desertification may be due to the increase of vegetation growth in temperate desert steppe and temperate steppe in north China, which are deemed as main source areas for sandstorms (Zou and Zhai, 2004). 3.2. Correlation between mean growing season NDVI and climate variables The coefficients of correlation between growing season NDVI and climatic variables differed in the different grassland types. For temperate meadow, growing season NDVI correlated strongly with temperature (R ¼ 0.76, Po0.001), and weakly with precipitation (R ¼ 0.07, P ¼ 0.79) and Im (R ¼ 0.05, P ¼ 0.832). In contrast, that of temperate desert steppe more closely related with precipitation (R ¼ 0.56, P ¼ 0.016) and Im (R ¼ 0.59, P ¼ 0.010) than with temperature (R ¼ 0.19, P ¼ 0.441). Growing season NDVI of temperate steppe significantly correlated with precipitation (R ¼ 0.63, P ¼ 0.005), Im (R ¼ 0.53, P ¼ 0.023), and temperature (R ¼ 0.56, P ¼ 0.015). The different growth environments contribute to these differences. Temperate meadow mainly appears under conditions of abundant precipitation and low temperature, in which temperature probably is the limiting factor for its vegetation growth. Temperate desert steppe is dominated under a dry climate, and thus likely limited by precipitation. The differences found in the relationships between NDVI and climates for the different grassland types can be interpreted phytophysiologically (Nemani et al., 2003), and are congruent with the results of several other studies (Braswell et al., 1997; Wang et al., 2003; Zhou et al., 2003). To further analyze the relationship between NDVI and climatic variables, we calculated Pearson correlation coefficients between NDVI and the two climatic variables (temperature and precipitation) for each grid cell. The correlation between NDVI and precipitation was strengthened with increasing precipitation at growing season precipitation below 200 mm, a precipitation threshold of grassland biome proposed by Tucker et al. (1991). On the other hand, the NDVI-precipitation relationship weakened when growing season precipitation fell below or exceeded 200 mm (Fig. 4(a)). This implies that vegetation growth is most sensitive to precipitation when the annual precipitation is about 200 mm. In extremely arid areas, strong evaporation processes ensure that slight increases in precipitation have only limited effect on vegetation growth. As precipitation increases, the relationship between NDVI Coefficients of correlation between NDVI and precipitation S. Piao et al. / Global Environmental Change 16 (2006) 340–348 1 0.5 0 -0.5 Y= -7E-06x2+0.031x+0.12 R2=0.34, P<0.001 -1 0 100 200 300 (b) 400 500 600 Growing season precipitation (mm) (a) Coefficients of correlation between NDVI and temperature 344 1 0.5 0 -0.5 Y= -0.03x+0.517 R2=0.08, P<0.001 -1 6 9 12 15 18 21 24 Growing season temperature (˚C) Fig. 4. Change in (a) correlation between growing season NDVI and precipitation with growing season precipitation, and (b) correlation between growing season NDVI and temperature with growing season mean temperature. and precipitation becomes closer, up to approximately 200 mm. After that value, greater precipitation increasingly fails to raise NDVI, perhaps indicating the limits of the water-use capacity of temperate grasslands. Alternatively, this may arise from the lower temperature and sunshine levels as precipitation increases. In contrast, the temperature-NDVI relationship exhibited a strictly linear relationship. The Pearson correlation coefficient between NDVI and temperature decreased with increasing temperature (R ¼ 0.37, Po0.001), indicating that the positive effect of temperature on the growth of grassland decreased as temperature rose (Fig. 4(b)). This implies that the sensitivity of vegetation in high-cold regions to temperature may decline under global warming conditions. The negative correlation between NDVI and temperature found in areas with high temperature may be a result of increased temperature which accelerates water evaporation, causes water scarcity, and therefore restricts vegetation growth (Jobbagy et al., 2002). 3.3. Trends in seasonal NDVI To recognize the changes in seasonal NDVI, we calculated the annual increase and increasing rates of seasonal NDVI by the study area and by vegetation type, as well as the corresponding coefficients of correlation ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 345 Table 1 Mean increase of seasonal NDVI (*0.01) (S), mean increase rate of seasonal NDVI (% yr1) (B), and coefficients of correlation between seasonal NDVI and year (R) for different grassland types during 1982–1999 (R ¼ 0.47 and R ¼ 0.59 correspond statistically to 5% and 1% significance levels, respectively) Vegetation type Spring Summer Autumn S B R S B R S B R Temperature meadow Temperature steppe Temperature desert steppe 0.19 0.10 0.07 0.81 0.53 0.55 0.55 0.57 0.52 0.13 0.19 0.12 0.29 0.53 0.72 0.56 0.55 0.56 0.16 0.16 0.12 0.52 0.59 0.80 0.57 0.53 0.63 Total 0.11 0.61 0.61 0.16 0.49 0.60 0.14 0.60 0.62 between NDVI and time in the period of 1982–1999 (Table 1). Rising trends were noticeable in the average NDVI of the study area for all the three seasons, implying that all these seasons contributed to the increase in growing season NDVI. Among the three seasons, summer had the largest total increase with an annual increase of 0.0016 yr1. The increasing amount of spring NDVI was minimal at 0.0011 yr1, but exhibited the fastest increasing rate. Annual increase rate for spring NDVI was about 0.61% yr1. The average annual increase and rate of autumn NDVI for the study area were 0.0014 yr1 and 0.6% yr1, respectively. The average seasonal NDVI increased for all the three seasons and for all three grassland types. Temperate meadow tended to have the largest NDVI increase amount (0.0019 yr1) and rate of increase (0.81% yr1) in spring. Summer showed relatively low total NDVI increase amount (0.0013 yr1), with a rate of increase less than half that of spring. Although there is a clear increase in spring NDVI for both temperate steppe and temperate desert steppe, the magnitude of increase was less than that of the other two seasons. For temperate steppe, summer was the season with the largest annual mean increase (0.0019 yr1), and the rate of NDVI increase peaked in autumn, at 0.59% yr1. For temperate desert steppe, annual average increases amount of NDVI in summer and autumn were almost equal (0.0012 yr1), but the rate of increase in autumn exceeded that of both spring and summer. This suggests the growing season of temperate desert steppe has been significantly prolonged. 3.4. Correlation between climates and NDVI in different seasons Table 2 summarizes coefficients of correlation between seasonal NDVI and corresponding climatic variables (temperature and precipitation) of the whole study area and each vegetation type. Considering the lag in response of vegetation growth to climate change (Braswell et al., 1997; Los et al. 2001; Wang et al., 2003), we also computed correlation coefficients between seasonal NDVI and climatic variables in the preceding season. For the whole study area, spring NDVI correlated positively not only with spring temperature (R ¼ 0.66, P ¼ 0.003), but also with non-growing season precipitation (R ¼ 0.54, P ¼ 0.021). The increase of precipitation during the non-growing season may improve the efficiency of soil moisture and benefit to the growth of temperate grassland in spring. Summer NDVI positively correlated with summer precipitation (R ¼ 0.49, P ¼ 0.04), but no obvious lag effect of spring climate on summer NDVI was observed. Autumn NDVI has significant positive correlation with autumn temperature (R ¼ 0.48, P ¼ 0.046) and with summer precipitation (R ¼ 0.52, P ¼ 0.026), but correlated only weakly with autumn precipitation (R ¼ 0.06, P ¼ 0.826). For temperate meadow, the correlation between NDVI and temperature was stronger than that between NDVI and precipitation in each of the three seasons, suggesting that temperature may be the key factor for growth of temperate meadow. Spring NDVI of temperate steppe correlated in a significant, positive fashion with both spring temperature and precipitation, suggesting that increases in either spring temperature or precipitation boost vegetation growth. In addition, temperature and precipitation increases in the non-growing season may also have some positive effects on vegetation growth in spring (R ¼ 0.45, P ¼ 0.06; and R ¼ 0.31, P ¼ 0.21). Summer NDVI correlated strongly with summer precipitation (R ¼ 0.53, P ¼ 0.025), but had no significant correlation with summer temperature (R ¼ 0.28, P ¼ 0.27). Autumn NDVI was mainly coupled with summer precipitation and fall temperature (R ¼ 0.64, P ¼ 0.004; R ¼ 0.54, P ¼ 0.02, respectively). Seasonal NDVI of temperate desert steppe did not show a significant correlation with temperature in any season. However, the correlation between NDVI and precipitation was notable and appeared to exhibit strong lag effects. For example, spring NDVI positively correlated with spring precipitation (R ¼ 0.34, P ¼ 0.16), but the relationship with precipitation in non-growing season was stronger (R ¼ 0.88, Po0.001), implying that precipitation in nongrowing season is probably critical to spring vegetation growth in temperate desert steppe. Similarly, increased summer precipitation enhanced plant growth in these ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 346 Table 2 Correlation coefficients between seasonal mean NDVI and precipitation and temperature for different grassland types (R ¼ 0.47 and 0.59 correspond statistically to 5% and 1% significance levels, respectively) Vegetation type Spring Summer Autumn Ra Ra1 Te Te1 Ra Ra1 Te Te1 Ra Ra1 Te Te1 Temperature meadow Temperature steppe Temperature desert steppe 0.13 0.58 0.34 0.30 0.31 0.89 0.78 0.70 0.26 0.36 0.45 0.23 0.15 0.53 0.57 0.01 0.31 0.65 0.64 0.28 0.34 0.04 0.16 0.03 0.00 0.07 0.09 0.06 0.64 0.80 0.24 0.54 0.11 0.36 0.12 0.13 Total 0.42 0.54 0.66 0.38 0.49 0.32 0.42 0.13 0.06 0.52 0.48 0.26 Ra: correlation coefficient between seasonal NDVI and precipitation in the same season; Ra1: correlation coefficient between seasonal NDVI and precipitation in the previous season; Te: correlation coefficient between seasonal NDVI and temperature in the same season; Te1: correlation coefficient between seasonal NDVI and temperature in the previous season. regions in summer and autumn (R ¼ 0.57, P ¼ 0.014; R ¼ 0.80, Po0.001, respectively). Such variations in the correlation between NDVI and climates with seasons have been observed in a few recent studies. For instance, Wang et al. (2003) documented that early and later seasons of vegetation growth were positively correlated with temperature, while NDVI in the middle growing season was negatively related with temperature in the Great Plains region of North America. Our study also found temporal lags in vegetation response to climate change, which has been widely observed in other regions (Braswell et al., 1997; Potter and Brooks, 1998; Los et al., 2001; Piao et al., 2003; Wang et al., 2003). However, the range of these lags is likely to vary spatially and temporally. Braswell et al. (1997) reported that NDVI at the global scale had a 2-year lag response to temperature. At the biome scale, Piao et al. (2003) concluded that the lag time of vegetation response to temperature was only 3 months in China. Wang et al. (2003) observed a 2- to 4week temporal lag in NDVI to the change of precipitation in the Great Plains area in America. Our present study documented that spring and autumn NDVI all positively correlated with the precipitation of the preceding season (both the non-growing season and summer) for temperate steppe and temperate desert steppe, suggesting an approximate three-month temporal lag. 4. Conclusions Correlation analyses between NDVI and climate variable is a powerful tool for probing ecosystem function response to global climate change (Potter and Brooks, 1998). The present study combined datasets of NDVI from 1982 to 1999 and climate parameters to analyze year-toyear variations in temperate grassland productivity and its relationship with climate in China. The results demonstrate that the vegetation productivity of China’s temperate grassland rose in the late 1990s compared to the early 1980s, and changes in climate likely function as major controllers for interannual variations in vegetation productivity. The effects of changes in climate on vegetation growth varied among different vegetation types, seasons and climates. Specifically, the coefficient of correlation between NDVI and the precipitation in growing seasons peaked at 200 mm per growing season, perhaps serving as an indicator of temperate grassland sensitivity. Additionally, a 3-month lag in the response of China’s temperate grasslands to climate change was identified. Our results suggest that more detailed information is necessary when using satellite data to quantify relationships between variations in vegetation activity and climate at a large scale. While some general goals have been achieved in this study, there are a few points that are still unclear and should be addressed. First, despite a significant increase in the vegetation growth and biomass production of China’s temperate grassland over the last two decades, we still do not know how the net carbon productivity changes because recent global warming also accelerates carbon loss through soil heterotrophic respiration (Raich et al., 2002). Further studies are needed to characterize the role of China’s temperate grassland ecosystems in the regional carbon balance. Second, as well as climate variables, atmospheric CO2 fertilization effect (Schimel et al., 2001), increased nutrient deposition (Holland et al., 1997), and human activity such as grazing management, land abandonment due to migration into urban areas (Runnström, 2000) may also partly account for the observed NDVI changes. Elevated atmospheric CO2 concentration will lead to increase in water use efficiency through decrease in stomatal conductance, and thereby promote vegetation growth particularly in water stressed ecosystems (Graham and Nobel, 1996). Quantifying the relative roles of these myriad factors on vegetation growth is an extraordinary challenge for developing strategies for the sustainable use of grassland resources in China. Finally, there is a large uncertainty in the prediction of future trends in vegetation productivity. We do not know yet how long will such greening trend of China’s temperate grasslands be persistent, which depends in part on future climate changes. Acknowledgments The authors wish to thank L.M. Zhou for providing the GIMMS NDVI data, F.Y. Wei for collecting the climate ARTICLE IN PRESS S. Piao et al. / Global Environmental Change 16 (2006) 340–348 data, and Dan Flynn and anonymous reviewers for their constructive comments on the manuscript. 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