Using GIMMS NDVI time series to estimate the impacts of grassland vegetation cover on surface air temperatures in the temperate grassland region of China

Abstract

Exploring the impacts of vegetation cover on regional climate is very important to assess the relationship between vegetation and climate, and to predict regional climate change in vegetated areas. Using the time series of Global Inventory Modelling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI), we estimated the impacts of grassland vegetation cover on surface air temperatures based on the observation minus reanalysis (OMR) approach. The theory of the OMR approach is that reanalysis data are not sensitive to local land surface properties, thus the changes in OMR can be attributed to land effects. The method used in this letter is that rates of change in OMR are regressed (using stepwise regression) on average NDVI and the rate of NDVI change. We applied this multiple stepwise regression analysis in the temperate grassland region of China during the growing season from 1982 to 2005. To evaluate the accuracy of regress equations, we estimated the monthly OMR temperatures during 1983–2005 and then compared the monthly estimated and actual OMR temperatures. The results showed that the estimated OMR temperatures were well consistent with actual OMR temperatures in all the months, with the correlation coefficients between them ranging from 0.83 to 0.98. It indicates that combined with the OMR approach, GIMMS NDVI can be used to accurately estimate the impacts of grassland vegetation on surface air temperatures in temperate grassland areas of China.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant number 41330640].