Abstract:

The influence of climate change on the terrestrial vegetation health (condition) is one of the most significant problems of global change study. The vegetation activity plays a key role in the global carbon cycle. The authors investigated the relationship of the advanced very high resolution radiometer-normalized difference vegetation index (AVHRR-NDVI) with the large-scale climate variations on the inter-annual time scale during the period 1982-2000 for the growing seasons (April-October). A singular value decomposition analysis was applied to the NDVI and surface air temperature data in the time-domain to detect the most predominant modes coupling them. The first paired-modes explain 60.9%, 39.5% and 24.6% of the squared covariance between NDVI and temperature in spring (April-May), summer (June-August), and autumn (September-October), respectively, which implies that there is the highest NDVI sensitivity to temperature in spring and the lowest in autumn. The spatial centers, as revealed by the maximum or minimum vector values corresponding to the leading singular values, indicate the high sensitive regions. Only considering the mode 1, the sensitive center for spring is located in western Siberia and the neighbor eastern Europe with a sensitivity of about 0.308 0 NDVI/℃. For summer, there are no predominantly sensitive centers, and on average for the relatively high center over 1000-1200 E by 450-600 N, the sensitivity is 0.248 0 NDVI/℃. For autumn, the center is located over the high latitudes of eastern Asia (1100-1400 E, 550-650 N), and the sensitivity is 0.087 5 NDVI/℃. The coherent patters as revealed by the singular decomposition analysis remain the same when coarser resolution NDVI data were used, suggesting a robust and stable climate/vegetation relationship.

Key words: normalized difference vegetation index (NDVI), climate change, sensitivity, large-scale