Abstract
Land surface temperature (LST) is an important parameter that affects the water cycle, environmental changes, and energy balance at global and regional scales. Herein, a time series analysis was conducted to estimate the monthly, seasonal, and interannual variations in LST during 2001–2019 in the Tarim Basin, China. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) LST, air temperature, air pressure, relative humidity, wind speed, precipitation, elevation, and land-cover type data, we analyzed the spatio-temporal change characteristics of LST and the influencing factors. High LSTs occurred in the desert and plains and low LSTs occurred in surrounding mountain regions. The highest LST was recorded in July (25.1 °C) and the lowest was in January (−9.5 °C). On a seasonal scale, LST decreased in the order: summer > spring > autumn > winter. Annual LST showed an increasing trend of 0.2 °C/10 a in the desert and mountain areas, while the plains indicated a decreasing trend. In spring and autumn, western regions were dominated by a downward trend, whereas in winter a downward trend occurred in eastern regions. In summer, areas covered by vegetation were dominated by a downward trend, and desert and bare lands were dominated by an upward trend. Random forest (RF) model analysis showed that elevation was the most significant influencing factor (22.1%), followed by mean air temperature (20.1%). Correlation analysis showed that the main climatic factors air temperature, relative humidity, and elevation have a good correlation with the LST. Land-cover type also affected LST; during February–December the lowest LST was observed for permanent glacier snow and the highest was observed in the desert. El Nino and La Nina greatly influenced the LST variations. The North Atlantic Oscillation and Pacific Decadal Oscillation indices were consistent with the mean LST anomaly, indicating their considerable influence on LST variations.
Highlights
Land surface temperature (LST) can be obtained from ground observations, remote sensing data retrieval, and reanalysis data based on surface energy balance model estimations
We matched the results of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1) with the observed data
High LSTs occurred in the desert and plains of the Tarim Basin (TB), while low LSTs occurred in surrounding mountain regions
Summary
Land surface temperature (LST) refers to the temperature of the soil, water, buildings, and the vegetation canopy on the land surface [1], is a key parameter for describing thermal conditions [2] and is a common research topic in local and global environmental studies [3]. LST plays an important role in a variety of scientific studies, such as those on hydrology, ecology, and global climate change [4]. LST can be obtained from ground observations, remote sensing data retrieval, and reanalysis data based on surface energy balance model estimations. Ground observations have high accuracy and temporal resolution, their point-scale representativeness and the sparse distribution of meteorological stations are major limitations for their research and application at regional to global scales
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