Publications

Bera, D; Das Chatterjee, N; Ghosh, S; Dinda, S; Bera, S (2022). Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India. JOURNAL OF CLEANER PRODUCTION, 379, 134735.

Abstract
Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027-0.075 degrees C/year) over a large part of the study area (33.934%-100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%-94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%-98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

DOI:
10.1016/j.jclepro.2022.134735

ISSN:
1879-1786