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.
Highlights
Land surface temperature (LST) and its spatiotemporal variations are crucial in studying land surface energy and water balance on the regional and global scale [1,2,3,4,5,6,7] and are key parameters in the International Geosphere and Biosphere Program (IGBP) [8]
With growing concern about global warming, this study aims to verify the locations where China’s terrestrial ecosystems are warming, the degree to which LST has increased, and the effects of the LST increase on vegetation
The mean annual LST (MAT) of 582 meteorological stations was used to validate the LST simulated by the Advanced Very High-Resolution Radiometer (AVHRR) images
Summary
Land surface temperature (LST) and its spatiotemporal variations are crucial in studying land surface energy and water balance on the regional and global scale [1,2,3,4,5,6,7] and are key parameters in the International Geosphere and Biosphere Program (IGBP) [8]. The NOAA polar-orbiting series satellite has been successfully used to observe the earth for over 30 years; several algorithms have been developed to retrieve LST based on the NOAA-Advanced Very High-Resolution Radiometer (AVHRR) [13,14,15,16,17]. The AVHRR data are highly advantageous in data acquisition characterized by an extended period as well as continuous and sufficiently high temporal resolution, especially in the untraversed regions without a meteorological station
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