Snow cover and vegetation greenness with leaf water content control the global land surface temperature

Azad Rasul () and Ramesh Ningthoujam
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Azad Rasul: Soran University
Ramesh Ningthoujam: Imperial College London

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 10, No 23, 14722-14748

Abstract: Abstract The land surface temperature (LST) and land use land cover (LULC) are the major components of climate- and environment-related studies. The objective of this study was to assess the relationship of LST with remotely sensed LULC-derived vegetation indices during 2018 at the global, latitudinal and continental scales. Moderate resolution imaging spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices (NDVI, EVI, LAI, DSI, NDWI, albedo, NDSI, NDBI) were processed using Earth Engine Code Editor. The analysis was conducted using correlation coefficient and significance of the relationship of variables based on 2050 randomly selected points at the global scale. Based on the univariate and geographically weighted regression methods, the research confirmed that vegetation greenness (NDVI), leaf water content (NDWI) and snow cover (DSI) are the codominant drivers of decreasing LST at the global scale including Europe, Asia, South America and North America at the continental scale. Snow cover during winter and vegetation greenness in summer seasons control the global LST. Although albedo shows an inverse relationship, NDBI and NDSI displayed a positive relationship to LST at the global scale. In conclusion, temporal seasonal and inter-annual dynamics of LST in response to snow cover and vegetation properties (greenness, moisture) should be focused on understanding and regulating LST at varying scales.

Keywords: Google Earth Engine; Global LST; Land surface temperature; Land use land cover; MODIS; Vegetation indices (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-021-01269-4

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