In this paper, we examined the elevation-dependent warming (EDW) patterns of MODIS LST across different seasons in the Qinling-Daba Mountains, further investigate the connections between the EDW patterns of Land surface temperature (LST) and land surface albedo (ALB) as well as aerosol optical depth (AOD). The key findings include: (1) Our study reveals a robust correlation between LST and air temperature in the Qinling-Daba Mountains, suggesting the feasibility of using MODIS LST to predict the temperature trends (2) During the period from 2001 to 2010, MODIS LST shows a significant EDW trend, primarily in the spring season. In contrast, a negative EDW is observed in the period during 2011-2021, which is contrary to the earlier decade, particularly during the autumn and winter seasons. (3) EDW of MODIS LST is affected by the combination of ALB and AOD. The former has a negative influence on the change of LST, particularly above 2500 m in elevation. However, the latter is negatively correlated with the trend of MODIS LST, primarily at lower and middle altitudes (0-2500 m). This study gives a comprehensive explanation for the EDW of the temporal variations of LST in the Qinling-Daba Mountains to improve our understanding of the complex interactions and potential future climate scenarios in the region.
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http://dx.doi.org/10.1038/s41598-024-75835-x | DOI Listing |
Environ Res
January 2025
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China.
Heat extremes become increasingly frequent and severe, posing adverse risks to public health and environment. Previous research on extreme heat mostly used meteorological observations or reanalysis data, which cannot well capture detailed spatial patterns. This study developed a seamless air temperature (T) dataset from remote sensing data to characterize the spatio-temporal variations of heat extremes in the Yangtze River Delta (YRD) from 2001 to 2023.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Southwest Institute of Survey and Design State Forestry and Grassland Administration, Kunming, 650031, China.
High-altitude areas are thought to be more sensitive to climate change, but long-term series of land surface temperature (LST) observations are still inadequate in low-latitude high-altitude mountainous areas. We investigated spatiotemporal variations in the LST and its dominant driving factors at different time scales based on the long-term series (2001 - 2020) of MODIS data over the Yunnan Province (YNP) in southwest China, with a special focus on elevation-dependent warming (EDW). The results indicated that annual LST generally increased at a rate of 0.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Republic of Korea.
The near-surface air temperature (NSAT) is crucial for understanding thermal and urban environments. Traditional estimation methods using general remote sensing images often focus on the types of spatial data or machine learning models used, neglecting the importance of seasonal and temporal variations, limiting their accuracy. This study introduces a novel ensemble model that incorporates both seasonal and temporal information integrated with satellite-derived land surface temperature (LST) data to enhance NSAT estimation, along with a rigorous feature importance analysis to identify the most impactful parameters.
View Article and Find Full Text PDFThe hybrid nature of the mid-infrared (MIR) spectrum complicates the separation of reflected solar irradiance from total energy. Consequently, existing studies rarely use MIR satellite data alone for retrieving land surface temperature (LST) and land surface emissivity (LSE). In this study, we developed What we believe to be a novel physics-based approach to retrieve LSE and LST using MIR channel data from the MEdium Resolution Spectral Imager II (MERSI-II) onboard China's new-generation polar-orbiting meteorological satellite Fengyun-3D (FY-3D).
View Article and Find Full Text PDFEnviron Monit Assess
November 2024
Department of Geography and Environmental Studies, GIS and Remote Sensing, University of Gondar, Gondar, Ethiopia.
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale.
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