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.
Aerosol optical depth (AOD) is a key parameter that reflects aerosol characteristics. However, research on the AOD of dust aerosols and various environmental variables is scarce. Therefore, we conducted in-depth studies on the distributions and variations of AOD in the Taklimakan Desert and its margins, China. We examined the correlation characteristics between AOD and meteorological factors combined with satellite remote sensing detection methods using MCD19A2-MODIS AOD products (from 2000, 2005, 2010, and 2015), MOD13Q1-MODIS normalized difference vegetation index products, and meteorological data. We analyzed the temporal and spatial distributions of AOD, periodic change trends, and important impacts of meteorological factors on AOD in the Taklimakan Desert and its margins. To explore the relationships between desert aerosols and meteorological factors, a random forest model was used along with environmental variables to predict AOD and rank factor contributions. Results indicated that the monthly average AOD exhibited a clear unimodal curve that reached its maximum in April. The AOD values followed the order spring (0.28) > summer (0.27) > autumn (0.18) > winter (0.17). This seasonality is clear and can be related to the frequent sandstorms occurring in spring and early summer. Interannual AOD showed a gradually increasing trend to 2010 then large changes to 2015. AOD tends to increase from south to north. Based on the general trend, the maximum value of AOD is more dispersed and its low-value area is always stable. The climatic index that has the most significant effect on AOD is relative humidity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.