Spatio-Temporal Changes of Land Surface Temperature and the Influencing Factors in the Tarim Basin, Northwest China

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.

Evolution Analysis of the Coupling Coordination of Microclimate and Landscape Ecological Risk Degree in the Xiahuayuan District in Recent 20 Years

“…During the transformation, the urban microclimate and landscape ecological risks significantly changed, which affected urban development, the ecological environment and the well-being of human life. The previous literature has shown that landscape ecological risk and LST are associated with road distance [25,33], urban expansion [34][35][36][37], climate change and human activities [38][39][40]. However, mitigating effects on both factors have also been identified that are merely related to their own inherent qualities.…”

Satellite-Derived Land Surface Temperature Dynamics in the Context of Global Change—A Review

“…In addition to global warming, interannual climate variabilities, such as the El-Nino-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO) or the Pacific North American Pattern (PNA), are reflected in the LST time series. Abbas et al [6] observed a significant influence of NAO, ENSO and PDO on anomalies of mean LST over the Tarim Basin in Northwest China. Over Alberta, Canada, the PNA had the greatest influence on LST; however, none of the relationships between atmospheric oscillations and LST warming were significant [9].…”

“…To analyze the relationship between multiple variables (e.g., LST, ET and albedo), a multiple regression model can be applied [35]. In addition to classic statistical methods, machine learning techniques have also been employed to analyze influencing factors on LST, e.g., the Random Forest model [6].…”

“…Figure 1 provides an overview of the application fields of LST. LST has been used to study global warming [3][4][5][6][7][8][9][10][11][12][13][14][15][16], interannual climate variabilities [5,[7][8][9][10]14,17], heat waves [18][19][20], droughts [21][22][23][24][25], soil moisture dynamics [22,23,[26][27][28][29], forest dynamics [28,[30][31][32][33][34][35][36][37][38][39], general vegetation-temperature interactions [15,20,[24]…”

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

“…LST can be obtained via ground measurements or analysis of remote sensing data based on estimates of the Earth's energy balance model. Although terrestrial measurements are more accurate than satellite imagery, point observations and the dispersion of meteorological stations are the main limitations of their research and application on regional and global scales [24]. Because of that, obtaining LST on an extensive terrain or global scale was not possible until satellite thermal sensors were developed, yet it was difficult to obtain spatial, temporal, and spectral high-resolution satellite imagery because of the relationships between these resolutions [25].…”