Spatiotemporal Variation of Land Surface Temperature and Vegetation in Response to Climate Change Based on NOAA-AVHRR Data over China

Abstract

The dynamics of land surface temperature (LST) and its correlation with vegetation are crucial to understanding the effects of global climate change. This study intended to retrieve the LST of China, based on the NOAA-AVHRR images, by using a split-window algorithm. The spatiotemporal variation of LST, Normalized difference vegetation index (NDVI), and the correlation between the two was investigated in China from 1982–2016. Moreover, eight scenarios were established to explore the driving forces in vegetation variation. Results indicated that the LST increased by 0.06 °C/year in nearly 81.1% of the study areas. The NDVI with an increasing rate of 0.1%/year and occupied 58.6% of the study areas. By contrast, 41.4% of the study areas with a decreasing rate of 0.7 × 10−3/year, was mainly observed in northern China. The correlation coefficients between NDVI and LST were higher than that between NDVI and precipitation, and the increase in LST could stimulate vegetation growth. Most regions of China have experienced significant warming over the past decades, specifically, desertification happens in northern China, because it is getting drier. The synergy of LST and precipitation is the primary cause of vegetation dynamics. Therefore, long-term monitoring of LST and NDVI is necessary to better understand the adaptation of the terrestrial ecosystem to global climate change.