In this paper, we analyzed interannual variations of normalized difference vegetation index (NDVI) and their relationships with climatic variables (temperature and precipitation) and human activity in China between 1982 and 1999. Monthly and seasonal NDVI increased significantly at both the country and biome scales over the study period. NDVI shows the largest increase (14.4% during the 18 years and a trend of 0.0018 yr−1) over 85.9% of the total study area in spring and the smallest increase (5.2% with a trend of 0.0012 yr−1) over 72.2% of the area in summer. The NDVI trends show a marked heterogeneity corresponding to regional and seasonal variations in climates. There is about a 3‐month lag for the period between the maximum trend in temperature (February) and that in NDVI (April or May) at the country and biome scales. Human activity (urbanization and agricultural practices) also played an important role in influencing the NDVI trends over some regions. Rapid urbanization resulted in a sharp decrease in NDVI in the Yangtze River and Pearl River deltas, while irrigation and fertilization may have contributed to the increased NDVI in the North China plain.

Knapp and Smith (1) suggested that interannual variability in aboveground net primary production (ANPP) is not related to fluctuations in precipitation, based on analysis of data from 11 Long-Term Ecological Research sites across North America. This finding, if applicable to other regions, is crucial to climate change research, because it may necessitate revisions of projections of ecosystem responses to climate change (2, 3). To examine the relationship between variability in net primary production (NPP) and precipitation at a broad scale, a longterm normalized difference vegetation index (NDVI) data set derived from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA), coupled with a historical climate data set, should constitute a useful and powerful data source, because NDVI data are strongly correlated with terrestrial NPP and are frequently used as NPP predictors (4, 5).We used an annual mean NDVI data set over China to quantify temporal NPP variability relative to precipitation variation, and used coefficient of variation (CV) to express the magnitude of interannual variability in NDVI and precipitation. We then calculated CVs of these two variables for each pixel, with a resolution of 0.1°latitude by 0.1°longitude, for five biome groups across China-forest, grassland, desert, alpine vegetation, and cropland (6 )-using 1982 to 1999 NDVI and precipitation data compiled in China (7 ). We assumed that interannual variability in NDVI or NPP was related to temporal variability in precipitation if the correlation between CVs for NDVI or NPP and precipitation were identified as statistically significant. The CV value of NDVI for these five biome groups showed a large spatial variation, with a mean CV of 8.3% for the forest biome group, 10.4% for grasslands, 24.6% for desert areas, 12.7% for alpine vegetation, and 9.3 % for cropland. The largest variation occurred in the desert biome, followed by herbaceous vegetation (grasslands and alpine meadows); forests were the least variable. These results agree with those of Knapp and Smith (1). However, our statistical analysis also showed a significant positive correlation between the CV of NDVI and that of precipitation for all five biome groups (Fig. 1, A to E). The coefficient of correlation (r) was 0.43 for forest, 0.56 for grassland, 0.37 for desert, 0.31 for alpine vegetation, and 0.39 for cropland, with a strong correlation between mean CV of NDVI and that of precipitation for these five biome groups [r ϭ 0.95, p ϭ 0.012 (Fig. 1F)]. Moreover, the relationship between CV of NPP estimated based on the Carnegie-Ames-Stanford Approach (CASA) model (8, 9) and that of precipitation revealed trends similar to those implicit in Fig. 1. The r values were estimated at 0.53 for forest, 0.54 for grassland, 0.48 for desert, 0.37 for alpine vegetation, and 0.35 for cropland, with a highly significant correlation between mean NPP CV and mean precipitation CV for these five biome groups (r ϭ 0.97, p ϭ 0.005). These re...

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