Title: MODIS land surface temperature data for prediction of urban heat island effect

Authors: Mujtaba Shafi; Amit Jain; Irfan Rashid

Addresses: University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India ' University Institute of Computing, Chandigarh University, Gharuan, Mohali, Punjab, India ' Geoinformatics Program, Department of Earth Sciences, University of Kashmir,Srinagar (J&K), India

Abstract: The unplanned urbanisation especially in the developing countries has led to the urban heat islands (UHI) largely impacting the local climate. The present study was carried out to assess the capability of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) product at a spatial resolution of 1 km for capturing urban heat island phenomenon of Srinagar city located in North West Himalaya, India. Satellite images acquired from MODIS has a revisit time for one day. Our analysis indicated that the LST data at the four observation times for the month of July 2012 is higher in urban built-up area than those within the surrounding suburbs. The built-up area and thermal signatures, as extracted from MODIS showed a good correlation thus establishing a fact that urbanisation leads to UHI effect. We suggest ensemble approach using machine learning, neural networks that could be worked upon to obtain an optimal predictive framework.

Keywords: urban heat islands; UHI; MODIS; land surface temperature; LST; temperature prediction; machine learning; predictive analysis; modelling; simulation.

DOI: 10.1504/IJSAMI.2019.104628

International Journal of Sustainable Agricultural Management and Informatics, 2019 Vol.5 No.4, pp.270 - 280

Received: 01 Mar 2019
Accepted: 07 Aug 2019
Published online: 24 Jan 2020 *

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